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Sample records for validated prediction model

  1. Predictive validation of an influenza spread model.

    Directory of Open Access Journals (Sweden)

    Ayaz Hyder

    Full Text Available BACKGROUND: Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. METHODS AND FINDINGS: We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998-1999. Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type. Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. CONCLUSIONS: Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve

  2. Predictive Validation of an Influenza Spread Model

    Science.gov (United States)

    Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

    2013-01-01

    Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

  3. Validated predictive modelling of the environmental resistome.

    Science.gov (United States)

    Amos, Gregory C A; Gozzard, Emma; Carter, Charlotte E; Mead, Andrew; Bowes, Mike J; Hawkey, Peter M; Zhang, Lihong; Singer, Andrew C; Gaze, William H; Wellington, Elizabeth M H

    2015-06-01

    Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome.

  4. Assessing Discriminative Performance at External Validation of Clinical Prediction Models.

    Directory of Open Access Journals (Sweden)

    Daan Nieboer

    Full Text Available External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting.We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1 the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2 the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury.The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples and heterogeneous in scenario 2 (in 17%-39% of simulated samples. Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2.The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.

  5. Validating predictions from climate envelope models.

    Directory of Open Access Journals (Sweden)

    James I Watling

    Full Text Available Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species' distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967-1971 (t1 and evaluated using occurrence data from 1998-2002 (t2. Model sensitivity (the ability to correctly classify species presences was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on

  6. Validating predictions from climate envelope models

    Science.gov (United States)

    Watling, J.; Bucklin, D.; Speroterra, C.; Brandt, L.; Cabal, C.; Romañach, Stephanie S.; Mazzotti, Frank J.

    2013-01-01

    Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

  7. Validation of a tuber blight (Phytophthora infestans) prediction model

    Science.gov (United States)

    Potato tuber blight caused by Phytophthora infestans accounts for significant losses in storage. There is limited published quantitative data on predicting tuber blight. We validated a tuber blight prediction model developed in New York with cultivars Allegany, NY 101, and Katahdin using independent...

  8. Validation of a multi-objective, predictive urban traffic model

    NARCIS (Netherlands)

    Wilmink, I.R.; Haak, P. van den; Woldeab, Z.; Vreeswijk, J.

    2013-01-01

    This paper describes the results of the verification and validation of the ecoStrategic Model, which was developed, implemented and tested in the eCoMove project. The model uses real-time and historical traffic information to determine the current, predicted and desired state of traffic in a

  9. Predicting the ungauged basin : Model validation and realism assessment

    NARCIS (Netherlands)

    Van Emmerik, T.H.M.; Mulder, G.; Eilander, D.; Piet, M.; Savenije, H.H.G.

    2015-01-01

    The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

  10. Predicting the ungauged basin: model validation and realism assessment

    NARCIS (Netherlands)

    van Emmerik, Tim; Mulder, Gert; Eilander, Dirk; Piet, Marijn; Savenije, Hubert

    2015-01-01

    The hydrological decade on Predictions in Ungauged Basins (PUB) led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of

  11. Cross-Validation of Aerobic Capacity Prediction Models in Adolescents.

    Science.gov (United States)

    Burns, Ryan Donald; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Saint-Maurice, Pedro F; Welk, Greg J; Mahar, Matthew T

    2015-08-01

    Cardiorespiratory endurance is a component of health-related fitness. FITNESSGRAM recommends the Progressive Aerobic Cardiovascular Endurance Run (PACER) or One mile Run/Walk (1MRW) to assess cardiorespiratory endurance by estimating VO2 Peak. No research has cross-validated prediction models from both PACER and 1MRW, including the New PACER Model and PACER-Mile Equivalent (PACER-MEQ) using current standards. The purpose of this study was to cross-validate prediction models from PACER and 1MRW against measured VO2 Peak in adolescents. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years (Mean = 14.7 ± 1.3 years; 32 girls, 52 boys) who completed the PACER and 1MRW in addition to a laboratory maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered moderately strong (R = .74-0.78), and prediction error (RMSE) ranged from 5.95 ml·kg⁻¹,min⁻¹ to 8.27 ml·kg⁻¹.min⁻¹. Criterion-referenced agreement into FITNESSGRAM's Healthy Fitness Zones was considered fair-to-good among models (Kappa = 0.31-0.62; Agreement = 75.5-89.9%; F = 0.08-0.65). In conclusion, prediction models demonstrated moderately strong linear relationships with measured VO2 Peak, fair prediction error, and fair-to-good criterion referenced agreement with measured VO2 Peak into FITNESSGRAM's Healthy Fitness Zones.

  12. Predicting the ungauged basin: Model validation and realism assessment

    Directory of Open Access Journals (Sweden)

    Tim evan Emmerik

    2015-10-01

    Full Text Available The hydrological decade on Predictions in Ungauged Basins (PUB led to many new insights in model development, calibration strategies, data acquisition and uncertainty analysis. Due to a limited amount of published studies on genuinely ungauged basins, model validation and realism assessment of model outcome has not been discussed to a great extent. With this paper we aim to contribute to the discussion on how one can determine the value and validity of a hydrological model developed for an ungauged basin. As in many cases no local, or even regional, data are available, alternative methods should be applied. Using a PUB case study in a genuinely ungauged basin in southern Cambodia, we give several examples of how one can use different types of soft data to improve model design, calibrate and validate the model, and assess the realism of the model output. A rainfall-runoff model was coupled to an irrigation reservoir, allowing the use of additional and unconventional data. The model was mainly forced with remote sensing data, and local knowledge was used to constrain the parameters. Model realism assessment was done using data from surveys. This resulted in a successful reconstruction of the reservoir dynamics, and revealed the different hydrological characteristics of the two topographical classes. This paper does not present a generic approach that can be transferred to other ungauged catchments, but it aims to show how clever model design and alternative data acquisition can result in a valuable hydrological model for an ungauged catchment.

  13. Discrete fracture modelling for the Stripa tracer validation experiment predictions

    International Nuclear Information System (INIS)

    Dershowitz, W.; Wallmann, P.

    1992-02-01

    Groundwater flow and transport through three-dimensional networks of discrete fractures was modeled to predict the recovery of tracer from tracer injection experiments conducted during phase 3 of the Stripa site characterization and validation protect. Predictions were made on the basis of an updated version of the site scale discrete fracture conceptual model used for flow predictions and preliminary transport modelling. In this model, individual fractures were treated as stochastic features described by probability distributions of geometric and hydrologic properties. Fractures were divided into three populations: Fractures in fracture zones near the drift, non-fracture zone fractures within 31 m of the drift, and fractures in fracture zones over 31 meters from the drift axis. Fractures outside fracture zones are not modelled beyond 31 meters from the drift axis. Transport predictions were produced using the FracMan discrete fracture modelling package for each of five tracer experiments. Output was produced in the seven formats specified by the Stripa task force on fracture flow modelling. (au)

  14. Preventing patient absenteeism: validation of a predictive overbooking model.

    Science.gov (United States)

    Reid, Mark W; Cohen, Samuel; Wang, Hank; Kaung, Aung; Patel, Anish; Tashjian, Vartan; Williams, Demetrius L; Martinez, Bibiana; Spiegel, Brennan M R

    2015-12-01

    To develop a model that identifies patients at high risk for missing scheduled appointments ("no-shows" and cancellations) and to project the impact of predictive overbooking in a gastrointestinal endoscopy clinic-an exemplar resource-intensive environment with a high no-show rate. We retrospectively developed an algorithm that uses electronic health record (EHR) data to identify patients who do not show up to their appointments. Next, we prospectively validated the algorithm at a Veterans Administration healthcare network clinic. We constructed a multivariable logistic regression model that assigned a no-show risk score optimized by receiver operating characteristic curve analysis. Based on these scores, we created a calendar of projected open slots to offer to patients and compared the daily performance of predictive overbooking with fixed overbooking and typical "1 patient, 1 slot" scheduling. Data from 1392 patients identified several predictors of no-show, including previous absenteeism, comorbid disease burden, and current diagnoses of mood and substance use disorders. The model correctly classified most patients during the development (area under the curve [AUC] = 0.80) and validation phases (AUC = 0.75). Prospective testing in 1197 patients found that predictive overbooking averaged 0.51 unused appointments per day versus 6.18 for typical booking (difference = -5.67; 95% CI, -6.48 to -4.87; P < .0001). Predictive overbooking could have increased service utilization from 62% to 97% of capacity, with only rare clinic overflows. Information from EHRs can accurately predict whether patients will no-show. This method can be used to overbook appointments, thereby maximizing service utilization while staying within clinic capacity.

  15. Developing a model for validation and prediction of bank customer ...

    African Journals Online (AJOL)

    Credit risk is the most important risk of banks. The main approaches of the bank to reduce credit risk are correct validation using the final status and the validation model parameters. High fuel of bank reserves and lost or outstanding facilities of banks indicate the lack of appropriate validation models in the banking network.

  16. Developing and Validating a Predictive Model for Stroke Progression

    Directory of Open Access Journals (Sweden)

    L.E. Craig

    2011-12-01

    discrimination and calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice.

  17. Developing and validating a predictive model for stroke progression.

    Science.gov (United States)

    Craig, L E; Wu, O; Gilmour, H; Barber, M; Langhorne, P

    2011-01-01

    sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice.

  18. Developing and Validating a Predictive Model for Stroke Progression

    Science.gov (United States)

    Craig, L.E.; Wu, O.; Gilmour, H.; Barber, M.; Langhorne, P.

    2011-01-01

    calibration of the predictive model appear sufficiently high to provide accurate predictions. This study also offers some discussion around the validation of predictive models for wider use in clinical practice. PMID:22566988

  19. Validation of an Acoustic Impedance Prediction Model for Skewed Resonators

    Science.gov (United States)

    Howerton, Brian M.; Parrott, Tony L.

    2009-01-01

    An impedance prediction model was validated experimentally to determine the composite impedance of a series of high-aspect ratio slot resonators incorporating channel skew and sharp bends. Such structures are useful for packaging acoustic liners into constrained spaces for turbofan noise control applications. A formulation of the Zwikker-Kosten Transmission Line (ZKTL) model, incorporating the Richards correction for rectangular channels, is used to calculate the composite normalized impedance of a series of six multi-slot resonator arrays with constant channel length. Experimentally, acoustic data was acquired in the NASA Langley Normal Incidence Tube over the frequency range of 500 to 3500 Hz at 120 and 140 dB OASPL. Normalized impedance was reduced using the Two-Microphone Method for the various combinations of channel skew and sharp 90o and 180o bends. Results show that the presence of skew and/or sharp bends does not significantly alter the impedance of a slot resonator as compared to a straight resonator of the same total channel length. ZKTL predicts the impedance of such resonators very well over the frequency range of interest. The model can be used to design arrays of slot resonators that can be packaged into complex geometries heretofore unsuitable for effective acoustic treatment.

  20. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model.

    Science.gov (United States)

    Huang, Yanqi; He, Lan; Dong, Di; Yang, Caiyun; Liang, Cuishan; Chen, Xin; Ma, Zelan; Huang, Xiaomei; Yao, Su; Liang, Changhong; Tian, Jie; Liu, Zaiyi

    2018-02-01

    To develop and validate a radiomics prediction model for individualized prediction of perineural invasion (PNI) in colorectal cancer (CRC). After computed tomography (CT) radiomics features extraction, a radiomics signature was constructed in derivation cohort (346 CRC patients). A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen (CEA) level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation (separate from the cohort used to build the model) was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram. The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell's concordance index (c-index): 0.817; 95% confidence interval (95% CI): 0.811-0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination (c-index: 0.803; 95% CI: 0.794-0.812). Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

  1. Geographic and temporal validity of prediction models: Different approaches were useful to examine model performance

    NARCIS (Netherlands)

    P.C. Austin (Peter); D. van Klaveren (David); Y. Vergouwe (Yvonne); D. Nieboer (Daan); D.S. Lee (Douglas); E.W. Steyerberg (Ewout)

    2016-01-01

    textabstractObjective: Validation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods. Study Design and Setting: We

  2. External validation of the Cairns Prediction Model (CPM) to predict conversion from laparoscopic to open cholecystectomy.

    Science.gov (United States)

    Hu, Alan Shiun Yew; Donohue, Peter O'; Gunnarsson, Ronny K; de Costa, Alan

    2018-03-14

    Valid and user-friendly prediction models for conversion to open cholecystectomy allow for proper planning prior to surgery. The Cairns Prediction Model (CPM) has been in use clinically in the original study site for the past three years, but has not been tested at other sites. A retrospective, single-centred study collected ultrasonic measurements and clinical variables alongside with conversion status from consecutive patients who underwent laparoscopic cholecystectomy from 2013 to 2016 in The Townsville Hospital, North Queensland, Australia. An area under the curve (AUC) was calculated to externally validate of the CPM. Conversion was necessary in 43 (4.2%) out of 1035 patients. External validation showed an area under the curve of 0.87 (95% CI 0.82-0.93, p = 1.1 × 10 -14 ). In comparison with most previously published models, which have an AUC of approximately 0.80 or less, the CPM has the highest AUC of all published prediction models both for internal and external validation. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

  3. Validation of an internal hardwood log defect prediction model

    Science.gov (United States)

    R. Edward. Thomas

    2011-01-01

    The type, size, and location of internal defects dictate the grade and value of lumber sawn from hardwood logs. However, acquiring internal defect knowledge with x-ray/computed-tomography or magnetic-resonance imaging technology can be expensive both in time and cost. An alternative approach uses prediction models based on correlations among external defect indicators...

  4. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    Science.gov (United States)

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

  5. Comorbidity predicts poor prognosis in nasopharyngeal carcinoma: Development and validation of a predictive score model

    International Nuclear Information System (INIS)

    Guo, Rui; Chen, Xiao-Zhong; Chen, Lei; Jiang, Feng; Tang, Ling-Long; Mao, Yan-Ping; Zhou, Guan-Qun; Li, Wen-Fei; Liu, Li-Zhi; Tian, Li; Lin, Ai-Hua; Ma, Jun

    2015-01-01

    Background and purpose: The impact of comorbidity on prognosis in nasopharyngeal carcinoma (NPC) is poorly characterized. Material and methods: Using the Adult Comorbidity Evaluation-27 (ACE-27) system, we assessed the prognostic value of comorbidity and developed, validated and confirmed a predictive score model in a training set (n = 658), internal validation set (n = 658) and independent set (n = 652) using area under the receiver operating curve analysis. Results: Comorbidity was present in 40.4% of 1968 patients (mild, 30.1%; moderate, 9.1%; severe, 1.2%). Compared to an ACE-27 score ⩽1, patients with an ACE-27 score >1 in the training set had shorter overall survival (OS) and disease-free survival (DFS) (both P < 0.001), similar results were obtained in the other sets (P < 0.05). In multivariate analysis, ACE-27 score was a significant independent prognostic factor for OS and DFS. The combined risk score model including ACE-27 had superior prognostic value to TNM stage alone in the internal validation set (0.70 vs. 0.66; P = 0.02), independent set (0.73 vs. 0.67; P = 0.002) and all patients (0.71 vs. 0.67; P < 0.001). Conclusions: Comorbidity significantly affects prognosis, especially in stages II and III, and should be incorporated into the TNM staging system for NPC. Assessment of comorbidity may improve outcome prediction and help tailor individualized treatment

  6. Predictive Simulation of Material Failure Using Peridynamics -- Advanced Constitutive Modeling, Verification and Validation

    Science.gov (United States)

    2016-03-31

    AFRL-AFOSR-VA-TR-2016-0309 Predictive simulation of material failure using peridynamics- advanced constitutive modeling, verification , and validation... Self -explanatory. 8. PERFORMING ORGANIZATION REPORT NUMBER. Enter all unique alphanumeric report numbers assigned by the performing organization, e.g...for public release. Predictive simulation of material failure using peridynamics-advanced constitutive modeling, verification , and validation John T

  7. Basic Modelling principles and Validation of Software for Prediction of Collision Damage

    DEFF Research Database (Denmark)

    Simonsen, Bo Cerup

    2000-01-01

    This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software.......This report describes basic modelling principles, the theoretical background and validation examples for the collision damage prediction module in the ISESO stand-alone software....

  8. Cost prediction following traumatic brain injury: model development and validation.

    Science.gov (United States)

    Spitz, Gershon; McKenzie, Dean; Attwood, David; Ponsford, Jennie L

    2016-02-01

    The ability to predict costs following a traumatic brain injury (TBI) would assist in planning treatment and support services by healthcare providers, insurers and other agencies. The objective of the current study was to develop predictive models of hospital, medical, paramedical, and long-term care (LTC) costs for the first 10 years following a TBI. The sample comprised 798 participants with TBI, the majority of whom were male and aged between 15 and 34 at time of injury. Costing information was obtained for hospital, medical, paramedical, and LTC costs up to 10 years postinjury. Demographic and injury-severity variables were collected at the time of admission to the rehabilitation hospital. Duration of PTA was the most important single predictor for each cost type. The final models predicted 44% of hospital costs, 26% of medical costs, 23% of paramedical costs, and 34% of LTC costs. Greater costs were incurred, depending on cost type, for individuals with longer PTA duration, obtaining a limb or chest injury, a lower GCS score, older age at injury, not being married or defacto prior to injury, living in metropolitan areas, and those reporting premorbid excessive or problem alcohol use. This study has provided a comprehensive analysis of factors predicting various types of costs following TBI, with the combination of injury-related and demographic variables predicting 23-44% of costs. PTA duration was the strongest predictor across all cost categories. These factors may be used for the planning and case management of individuals following TBI. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

  9. Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial.

    Science.gov (United States)

    Collis, Joe; Connor, Anthony J; Paczkowski, Marcin; Kannan, Pavitra; Pitt-Francis, Joe; Byrne, Helen M; Hubbard, Matthew E

    2017-04-01

    In this work, we present a pedagogical tumour growth example, in which we apply calibration and validation techniques to an uncertain, Gompertzian model of tumour spheroid growth. The key contribution of this article is the discussion and application of these methods (that are not commonly employed in the field of cancer modelling) in the context of a simple model, whose deterministic analogue is widely known within the community. In the course of the example, we calibrate the model against experimental data that are subject to measurement errors, and then validate the resulting uncertain model predictions. We then analyse the sensitivity of the model predictions to the underlying measurement model. Finally, we propose an elementary learning approach for tuning a threshold parameter in the validation procedure in order to maximize predictive accuracy of our validated model.

  10. An approach to model validation and model-based prediction -- polyurethane foam case study.

    Energy Technology Data Exchange (ETDEWEB)

    Dowding, Kevin J.; Rutherford, Brian Milne

    2003-07-01

    Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

  11. External Validation of a Prediction Model for Successful External Cephalic Version

    NARCIS (Netherlands)

    de Hundt, Marcella; Vlemmix, Floortje; Kok, Marjolein; van der Steeg, Jan W.; Bais, Joke M.; Mol, Ben W.; van der Post, Joris A.

    2012-01-01

    We sought external validation of a prediction model for the probability of a successful external cephalic version (ECV). We evaluated the performance of the prediction model with calibration and discrimination. For clinical practice, we developed a score chart to calculate the probability of a

  12. Validation of models that predict Cesarean section after induction of labor

    NARCIS (Netherlands)

    Verhoeven, C. J. M.; Oudenaarden, A.; Hermus, M. A. A.; Porath, M. M.; Oei, S. G.; Mol, B. W. J.

    2009-01-01

    Objective Models for the prediction of Cesarean delivery after induction of labor can be used to improve clinical decision-making. The objective of this study was to validate two existing models, published by Peregrine et al. and Rane et al., for the prediction of Cesarean section after induction of

  13. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting

    Science.gov (United States)

    2014-01-01

    Background Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models. Methods We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures. Results 11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models. Conclusions The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling

  14. Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study

    NARCIS (Netherlands)

    Onland, Wes; Debray, Thomas P.; Laughon, Matthew M.; Miedema, Martijn; Cools, Filip; Askie, Lisa M.; Asselin, Jeanette M.; Calvert, Sandra A.; Courtney, Sherry E.; Dani, Carlo; Durand, David J.; Marlow, Neil; Peacock, Janet L.; Pillow, J. Jane; Soll, Roger F.; Thome, Ulrich H.; Truffert, Patrick; Schreiber, Michael D.; van Reempts, Patrick; Vendettuoli, Valentina; Vento, Giovanni; van Kaam, Anton H.; Moons, Karel G.; Offringa, Martin

    2013-01-01

    Bronchopulmonary dysplasia (BPD) is a common complication of preterm birth. Very different models using clinical parameters at an early postnatal age to predict BPD have been developed with little extensive quantitative validation. The objective of this study is to review and validate clinical

  15. Development and Validation of a Predictive Model for Functional Outcome After Stroke Rehabilitation: The Maugeri Model.

    Science.gov (United States)

    Scrutinio, Domenico; Lanzillo, Bernardo; Guida, Pietro; Mastropasqua, Filippo; Monitillo, Vincenzo; Pusineri, Monica; Formica, Roberto; Russo, Giovanna; Guarnaschelli, Caterina; Ferretti, Chiara; Calabrese, Gianluigi

    2017-12-01

    Prediction of outcome after stroke rehabilitation may help clinicians in decision-making and planning rehabilitation care. We developed and validated a predictive tool to estimate the probability of achieving improvement in physical functioning (model 1) and a level of independence requiring no more than supervision (model 2) after stroke rehabilitation. The models were derived from 717 patients admitted for stroke rehabilitation. We used multivariable logistic regression analysis to build each model. Then, each model was prospectively validated in 875 patients. Model 1 included age, time from stroke occurrence to rehabilitation admission, admission motor and cognitive Functional Independence Measure scores, and neglect. Model 2 included age, male gender, time since stroke onset, and admission motor and cognitive Functional Independence Measure score. Both models demonstrated excellent discrimination. In the derivation cohort, the area under the curve was 0.883 (95% confidence intervals, 0.858-0.910) for model 1 and 0.913 (95% confidence intervals, 0.884-0.942) for model 2. The Hosmer-Lemeshow χ 2 was 4.12 ( P =0.249) and 1.20 ( P =0.754), respectively. In the validation cohort, the area under the curve was 0.866 (95% confidence intervals, 0.840-0.892) for model 1 and 0.850 (95% confidence intervals, 0.815-0.885) for model 2. The Hosmer-Lemeshow χ 2 was 8.86 ( P =0.115) and 34.50 ( P =0.001), respectively. Both improvement in physical functioning (hazard ratios, 0.43; 0.25-0.71; P =0.001) and a level of independence requiring no more than supervision (hazard ratios, 0.32; 0.14-0.68; P =0.004) were independently associated with improved 4-year survival. A calculator is freely available for download at https://goo.gl/fEAp81. This study provides researchers and clinicians with an easy-to-use, accurate, and validated predictive tool for potential application in rehabilitation research and stroke management. © 2017 American Heart Association, Inc.

  16. Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model.

    Science.gov (United States)

    Schroeder, Emily B; Xu, Stan; Goodrich, Glenn K; Nichols, Gregory A; O'Connor, Patrick J; Steiner, John F

    2017-07-01

    To develop and externally validate a prediction model for the 6-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes. The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007-2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic health records. We developed 16-variable and 6-variable models, using a Cox counting model process that allows for the inclusion of multiple 6-month observation periods per person. Across the three cohorts, there were 850,992 6-month observation periods, and 10,448 periods with at least one severe hypoglycemic event. The six-variable model contained age, diabetes type, HgbA1c, eGFR, history of a hypoglycemic event in the prior year, and insulin use. Both prediction models performed well, with good calibration and c-statistics of 0.84 and 0.81 for the 16-variable and 6-variable models, respectively. In the external validation cohorts, the c-statistics were 0.80-0.84. We developed and validated two prediction models for predicting the 6-month risk of hypoglycemia. The 16-variable model had slightly better performance than the 6-variable model, but in some practice settings, use of the simpler model may be preferred. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Development and validation of a predictive model for excessive postpartum blood loss: A retrospective, cohort study.

    Science.gov (United States)

    Rubio-Álvarez, Ana; Molina-Alarcón, Milagros; Arias-Arias, Ángel; Hernández-Martínez, Antonio

    2018-03-01

    postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. Despite the use of uterotonics agents as preventive measure, it remains a challenge to identify those women who are at increased risk of postpartum bleeding. to develop and to validate a predictive model to assess the risk of excessive bleeding in women with vaginal birth. retrospective cohorts study. "Mancha-Centro Hospital" (Spain). the elaboration of the predictive model was based on a derivation cohort consisting of 2336 women between 2009 and 2011. For validation purposes, a prospective cohort of 953 women between 2013 and 2014 were employed. Women with antenatal fetal demise, multiple pregnancies and gestations under 35 weeks were excluded METHODS: we used a multivariate analysis with binary logistic regression, Ridge Regression and areas under the Receiver Operating Characteristic curves to determine the predictive ability of the proposed model. there was 197 (8.43%) women with excessive bleeding in the derivation cohort and 63 (6.61%) women in the validation cohort. Predictive factors in the final model were: maternal age, primiparity, duration of the first and second stages of labour, neonatal birth weight and antepartum haemoglobin levels. Accordingly, the predictive ability of this model in the derivation cohort was 0.90 (95% CI: 0.85-0.93), while it remained 0.83 (95% CI: 0.74-0.92) in the validation cohort. this predictive model is proved to have an excellent predictive ability in the derivation cohort, and its validation in a latter population equally shows a good ability for prediction. This model can be employed to identify women with a higher risk of postpartum haemorrhage. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Testing the Predictive Validity of the Hendrich II Fall Risk Model.

    Science.gov (United States)

    Jung, Hyesil; Park, Hyeoun-Ae

    2018-03-01

    Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

  19. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

    Science.gov (United States)

    Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

    2018-05-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

  20. Prediction and Validation of Heat Release Direct Injection Diesel Engine Using Multi-Zone Model

    Science.gov (United States)

    Anang Nugroho, Bagus; Sugiarto, Bambang; Prawoto; Shalahuddin, Lukman

    2014-04-01

    The objective of this study is to develop simulation model which capable to predict heat release of diesel combustion accurately in efficient computation time. A multi-zone packet model has been applied to solve the combustion phenomena inside diesel cylinder. The model formulations are presented first and then the numerical results are validated on a single cylinder direct injection diesel engine at various engine speed and timing injections. The model were found to be promising to fulfill the objective above.

  1. Can preventable adverse events be predicted among hospitalized older patients? The development and validation of a predictive model.

    NARCIS (Netherlands)

    Steeg, L. van de; Langelaan, M.; Wagner, C.

    2014-01-01

    Objective: To develop and validate a predictive model for preventable adverse events (AEs) in hospitalized older patients, using clinically important risk factors that are readily available on admission. Design: Data from two retrospective patient record review studies on AEs were used. Risk factors

  2. Validation of a risk prediction model for Barrett's esophagus in an Australian population.

    Science.gov (United States)

    Ireland, Colin J; Gordon, Andrea L; Thompson, Sarah K; Watson, David I; Whiteman, David C; Reed, Richard L; Esterman, Adrian

    2018-01-01

    Esophageal adenocarcinoma is a disease that has a high mortality rate, the only known precursor being Barrett's esophagus (BE). While screening for BE is not cost-effective at the population level, targeted screening might be beneficial. We have developed a risk prediction model to identify people with BE, and here we present the external validation of this model. A cohort study was undertaken to validate a risk prediction model for BE. Individuals with endoscopy and histopathology proven BE completed a questionnaire containing variables previously identified as risk factors for this condition. Their responses were combined with data from a population sample for analysis. Risk scores were derived for each participant. Overall performance of the risk prediction model in terms of calibration and discrimination was assessed. Scores from 95 individuals with BE and 636 individuals from the general population were analyzed. The Brier score was 0.118, suggesting reasonable overall performance. The area under the receiver operating characteristic was 0.83 (95% CI 0.78-0.87). The Hosmer-Lemeshow statistic was p =0.14. Minimizing false positives and false negatives, the model achieved a sensitivity of 74% and a specificity of 73%. This study has validated a risk prediction model for BE that has a higher sensitivity than previous models.

  3. Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

    NARCIS (Netherlands)

    Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

    Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

  4. Development and validation of multivariable models to predict mortality and hospitalization in patients with heart failure

    NARCIS (Netherlands)

    Voors, Adriaan A.; Ouwerkerk, Wouter; Zannad, Faiez; van Veldhuisen, Dirk J.; Samani, Nilesh J.; Ponikowski, Piotr; Ng, Leong L.; Metra, Marco; ter Maaten, Jozine M.; Lang, Chim C.; Hillege, Hans L.; van der Harst, Pim; Filippatos, Gerasimos; Dickstein, Kenneth; Cleland, John G.; Anker, Stefan D.; Zwinderman, Aeilko H.

    2017-01-01

    Introduction From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. Methods and results BIOSTAT-CHF is a research programme designed to

  5. Validation of Occupants’ Behaviour Models for Indoor Quality Parameter and Energy Consumption Prediction

    DEFF Research Database (Denmark)

    Fabi, Valentina; Sugliano, Martina; Andersen, Rune Korsholm

    2015-01-01

    Occupants’ behaviour related to building control system plays a significant role to achieve thermal comfort and air quality in naturally-ventilated buildings. Generally, the published models of occupant's behavior are not validated, meaning that the predictive power has not yet been tested. For t...

  6. External Validation of Prediction Models for Pneumonia in Primary Care Patients with Lower Respiratory Tract Infection

    DEFF Research Database (Denmark)

    Schierenberg, Alwin; Minnaard, Margaretha C; Hopstaken, Rogier M

    2016-01-01

    BACKGROUND: Pneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S mode...... discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use....

  7. Development and validation of a prediction model for loss of physical function in elderly hemodialysis patients.

    Science.gov (United States)

    Fukuma, Shingo; Shimizu, Sayaka; Shintani, Ayumi; Kamitani, Tsukasa; Akizawa, Tadao; Fukuhara, Shunichi

    2017-09-05

    Among aging hemodialysis patients, loss of physical function has become a major issue. We developed and validated a model of predicting loss of physical function among elderly hemodialysis patients. We conducted a cohort study involving maintenance hemodialysis patients  ≥65 years of age from the Dialysis Outcomes and Practice Pattern Study in Japan. The derivation cohort included 593 early phase (1996-2004) patients and the temporal validation cohort included 447 late-phase (2005-12) patients. The main outcome was the incidence of loss of physical function, defined as the 12-item Short Form Health Survey physical function score decreasing to 0 within a year. Using backward stepwise logistic regression by Akaike's Information Criteria, six predictors (age, gender, dementia, mental health, moderate activity and ascending stairs) were selected for the final model. Points were assigned based on the regression coefficients and the total score was calculated by summing the points for each predictor. In total, 65 (11.0%) and 53 (11.9%) hemodialysis patients lost their physical function within 1 year in the derivation and validation cohorts, respectively. This model has good predictive performance quantified by both discrimination and calibration. The proportion of the loss of physical function increased sequentially through low-, middle-, and high-score categories based on the model (2.5%, 11.7% and 22.3% in the validation cohort, respectively). The loss of physical function was strongly associated with 1-year mortality [adjusted odds ratio 2.48 (95% confidence interval 1.26-4.91)]. We developed and validated a risk prediction model with good predictive performance for loss of physical function in elderly hemodialysis patients. Our simple prediction model may help physicians and patients make more informed decisions for healthy longevity. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA.

  8. Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry.

    Science.gov (United States)

    Lee, Michael J; Cizik, Amy M; Hamilton, Deven; Chapman, Jens R

    2014-09-01

    The impact of surgical site infection (SSI) is substantial. Although previous study has determined relative risk and odds ratio (OR) values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of SSI, rather than relative risk or OR values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of SSI after spine surgery. This study performs a multivariate analysis of SSI after spine surgery using a large prospective surgical registry. Using the results of this analysis, this study will then create and validate a predictive model for SSI after spine surgery. The patient sample is from a high-quality surgical registry from our two institutions with prospectively collected, detailed demographic, comorbidity, and complication data. An SSI that required return to the operating room for surgical debridement. Using a prospectively collected surgical registry of more than 1,532 patients with extensive demographic, comorbidity, surgical, and complication details recorded for 2 years after the surgery, we identified several risk factors for SSI after multivariate analysis. Using the beta coefficients from those regression analyses, we created a model to predict the occurrence of SSI after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created a predictive model based on our beta coefficients from our multivariate analysis. The final predictive model for SSI had a receiver-operator curve characteristic of 0.72, considered to be a fair measure. The final model has been uploaded for use on SpineSage.com. We present a validated model for predicting SSI after spine surgery. The value in this model is that it gives

  9. Validating a model that predicts daily growth and feed quality of New Zealand dairy pastures.

    Science.gov (United States)

    Woodward, S J

    2001-09-01

    The Pasture Quality (PQ) model is a simple, mechanistic, dynamical system model that was designed to capture the essential biological processes in grazed grass-clover pasture, and to be optimised to derive improved grazing strategies for New Zealand dairy farms. While the individual processes represented in the model (photosynthesis, tissue growth, flowering, leaf death, decomposition, worms) were based on experimental data, this did not guarantee that the assembled model would accurately predict the behaviour of the system as a whole (i.e., pasture growth and quality). Validation of the whole model was thus a priority, since any strategy derived from the model could impact a farm business in the order of thousands of dollars per annum if adopted. This paper describes the process of defining performance criteria for the model, obtaining suitable data to test the model, and carrying out the validation analysis. The validation process highlighted a number of weaknesses in the model, which will lead to the model being improved. As a result, the model's utility will be enhanced. Furthermore, validation was found to have an unexpected additional benefit, in that despite the model's poor initial performance, support was generated for the model among field scientists involved in the wider project.

  10. Development and Validation of a Prediction Model to Estimate Individual Risk of Pancreatic Cancer.

    Science.gov (United States)

    Yu, Ami; Woo, Sang Myung; Joo, Jungnam; Yang, Hye-Ryung; Lee, Woo Jin; Park, Sang-Jae; Nam, Byung-Ho

    2016-01-01

    There is no reliable screening tool to identify people with high risk of developing pancreatic cancer even though pancreatic cancer represents the fifth-leading cause of cancer-related death in Korea. The goal of this study was to develop an individualized risk prediction model that can be used to screen for asymptomatic pancreatic cancer in Korean men and women. Gender-specific risk prediction models for pancreatic cancer were developed using the Cox proportional hazards model based on an 8-year follow-up of a cohort study of 1,289,933 men and 557,701 women in Korea who had biennial examinations in 1996-1997. The performance of the models was evaluated with respect to their discrimination and calibration ability based on the C-statistic and Hosmer-Lemeshow type χ2 statistic. A total of 1,634 (0.13%) men and 561 (0.10%) women were newly diagnosed with pancreatic cancer. Age, height, BMI, fasting glucose, urine glucose, smoking, and age at smoking initiation were included in the risk prediction model for men. Height, BMI, fasting glucose, urine glucose, smoking, and drinking habit were included in the risk prediction model for women. Smoking was the most significant risk factor for developing pancreatic cancer in both men and women. The risk prediction model exhibited good discrimination and calibration ability, and in external validation it had excellent prediction ability. Gender-specific risk prediction models for pancreatic cancer were developed and validated for the first time. The prediction models will be a useful tool for detecting high-risk individuals who may benefit from increased surveillance for pancreatic cancer.

  11. Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF in fish

    Directory of Open Access Journals (Sweden)

    Milan Chiara

    2010-07-01

    Full Text Available Abstract Background Bioconcentration factor (BCF describes the behaviour of a chemical in terms of its likelihood of concentrating in organisms in the environment. It is a fundamental property in recent regulations, such as the European Community Regulation on chemicals and their safe use or the Globally Harmonized System for classification, labelling and packaging. These new regulations consider the possibility of reducing or waiving animal tests using alternative methods, such as in silico methods. This study assessed and validated the CAESAR predictive model for BCF in fish. Results To validate the model, new experimental data were collected and used to create an external set, as a second validation set (a first validation exercise had been done just after model development. The performance of the model was compared with BCFBAF v3.00. For continuous values and for classification purposes the CAESAR BCF model gave better results than BCFBAF v3.00 for the chemicals in the applicability domain of the model. R2 and Q2 were good and accuracy in classification higher than 90%. Applying an offset of 0.5 to the compounds predicted with BCF close to the thresholds, the number of false negatives (the most dangerous errors dropped considerably (less than 0.6% of chemicals. Conclusions The CAESAR model for BCF is useful for regulatory purposes because it is robust, reliable and predictive. It is also fully transparent and documented and has a well-defined applicability domain, as required by REACH. The model is freely available on the CAESAR web site and easy to use. The reliability of the model reporting the six most similar compounds found in the CAESAR dataset, and their experimental and predicted values, can be evaluated.

  12. Validation and uncertainty analysis of a pre-treatment 2D dose prediction model

    Science.gov (United States)

    Baeza, Jose A.; Wolfs, Cecile J. A.; Nijsten, Sebastiaan M. J. J. G.; Verhaegen, Frank

    2018-02-01

    Independent verification of complex treatment delivery with megavolt photon beam radiotherapy (RT) has been effectively used to detect and prevent errors. This work presents the validation and uncertainty analysis of a model that predicts 2D portal dose images (PDIs) without a patient or phantom in the beam. The prediction model is based on an exponential point dose model with separable primary and secondary photon fluence components. The model includes a scatter kernel, off-axis ratio map, transmission values and penumbra kernels for beam-delimiting components. These parameters were derived through a model fitting procedure supplied with point dose and dose profile measurements of radiation fields. The model was validated against a treatment planning system (TPS; Eclipse) and radiochromic film measurements for complex clinical scenarios, including volumetric modulated arc therapy (VMAT). Confidence limits on fitted model parameters were calculated based on simulated measurements. A sensitivity analysis was performed to evaluate the effect of the parameter uncertainties on the model output. For the maximum uncertainty, the maximum deviating measurement sets were propagated through the fitting procedure and the model. The overall uncertainty was assessed using all simulated measurements. The validation of the prediction model against the TPS and the film showed a good agreement, with on average 90.8% and 90.5% of pixels passing a (2%,2 mm) global gamma analysis respectively, with a low dose threshold of 10%. The maximum and overall uncertainty of the model is dependent on the type of clinical plan used as input. The results can be used to study the robustness of the model. A model for predicting accurate 2D pre-treatment PDIs in complex RT scenarios can be used clinically and its uncertainties can be taken into account.

  13. Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

    Science.gov (United States)

    Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

    2017-10-01

    Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (PLearning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that

  14. Validations and improvements of airfoil trailing-edge noise prediction models using detailed experimental data

    DEFF Research Database (Denmark)

    Kamruzzaman, M.; Lutz, Th.; Würz, W.

    2012-01-01

    This paper describes an extensive assessment and a step by step validation of different turbulent boundary-layer trailing-edge noise prediction schemes developed within the European Union funded wind energy project UpWind. To validate prediction models, measurements of turbulent boundary-layer pr...... with measurements in the frequency region higher than 1 kHz, whereas they over-predict the sound pressure level in the low-frequency region. Copyright © 2011 John Wiley & Sons, Ltd.......-layer properties such as two-point turbulent velocity correlations, the spectra of the associated wall pressure fluctuations and the emitted trailing-edge far-field noise were performed in the laminar wind tunnel of the Institute of Aerodynamics and Gas Dynamics, University of Stuttgart. The measurements were...... carried out for a NACA 643-418 airfoil, at Re  =  2.5 ×106, angle of attack of −6° to 6°. Numerical results of different prediction schemes are extensively validated and discussed elaborately. The investigations on the TNO-Blake noise prediction model show that the numerical wall pressure fluctuation...

  15. Review and evaluation of performance measures for survival prediction models in external validation settings

    Directory of Open Access Journals (Sweden)

    M. Shafiqur Rahman

    2017-04-01

    Full Text Available Abstract Background When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. Methods An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Results Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell’s concordance measure which tended to increase as censoring increased. Conclusions We recommend that Uno’s concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller’s measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston’s D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive

  16. Validation of model predictions of pore-scale fluid distributions during two-phase flow

    Science.gov (United States)

    Bultreys, Tom; Lin, Qingyang; Gao, Ying; Raeini, Ali Q.; AlRatrout, Ahmed; Bijeljic, Branko; Blunt, Martin J.

    2018-05-01

    Pore-scale two-phase flow modeling is an important technology to study a rock's relative permeability behavior. To investigate if these models are predictive, the calculated pore-scale fluid distributions which determine the relative permeability need to be validated. In this work, we introduce a methodology to quantitatively compare models to experimental fluid distributions in flow experiments visualized with microcomputed tomography. First, we analyzed five repeated drainage-imbibition experiments on a single sample. In these experiments, the exact fluid distributions were not fully repeatable on a pore-by-pore basis, while the global properties of the fluid distribution were. Then two fractional flow experiments were used to validate a quasistatic pore network model. The model correctly predicted the fluid present in more than 75% of pores and throats in drainage and imbibition. To quantify what this means for the relevant global properties of the fluid distribution, we compare the main flow paths and the connectivity across the different pore sizes in the modeled and experimental fluid distributions. These essential topology characteristics matched well for drainage simulations, but not for imbibition. This suggests that the pore-filling rules in the network model we used need to be improved to make reliable predictions of imbibition. The presented analysis illustrates the potential of our methodology to systematically and robustly test two-phase flow models to aid in model development and calibration.

  17. Validation of water sorption-based clay prediction models for calcareous soils

    DEFF Research Database (Denmark)

    Arthur, Emmanuel; Razzaghi, Fatemeh; Moosavi, Ali

    2017-01-01

    on prediction accuracy. The soils had clay content ranging from 9 to 61% and CaCO3 from 24 to 97%. The three water sorption models considered showed a reasonably fair prediction of the clay content from water sorption at 28% relative humidity (RMSE and ME values ranging from 10.6 to 12.1 and −8.1 to −4......Soil particle size distribution (PSD), particularly the active clay fraction, mediates soil engineering, agronomic and environmental functions. The tedious and costly nature of traditional methods of determining PSD prompted the development of water sorption-based models for determining the clay...... fraction. The applicability of such models to semi-arid soils with significant amounts of calcium carbonate and/or gypsum is unknown. The objective of this study was to validate three water sorption-based clay prediction models for 30 calcareous soils from Iran and identify the effect of CaCO3...

  18. A Supervised Learning Process to Validate Online Disease Reports for Use in Predictive Models.

    Science.gov (United States)

    Patching, Helena M M; Hudson, Laurence M; Cooke, Warrick; Garcia, Andres J; Hay, Simon I; Roberts, Mark; Moyes, Catherine L

    2015-12-01

    Pathogen distribution models that predict spatial variation in disease occurrence require data from a large number of geographic locations to generate disease risk maps. Traditionally, this process has used data from public health reporting systems; however, using online reports of new infections could speed up the process dramatically. Data from both public health systems and online sources must be validated before they can be used, but no mechanisms exist to validate data from online media reports. We have developed a supervised learning process to validate geolocated disease outbreak data in a timely manner. The process uses three input features, the data source and two metrics derived from the location of each disease occurrence. The location of disease occurrence provides information on the probability of disease occurrence at that location based on environmental and socioeconomic factors and the distance within or outside the current known disease extent. The process also uses validation scores, generated by disease experts who review a subset of the data, to build a training data set. The aim of the supervised learning process is to generate validation scores that can be used as weights going into the pathogen distribution model. After analyzing the three input features and testing the performance of alternative processes, we selected a cascade of ensembles comprising logistic regressors. Parameter values for the training data subset size, number of predictors, and number of layers in the cascade were tested before the process was deployed. The final configuration was tested using data for two contrasting diseases (dengue and cholera), and 66%-79% of data points were assigned a validation score. The remaining data points are scored by the experts, and the results inform the training data set for the next set of predictors, as well as going to the pathogen distribution model. The new supervised learning process has been implemented within our live site and is

  19. Western Validation of a Novel Gastric Cancer Prognosis Prediction Model in US Gastric Cancer Patients.

    Science.gov (United States)

    Woo, Yanghee; Goldner, Bryan; Son, Taeil; Song, Kijun; Noh, Sung Hoon; Fong, Yuman; Hyung, Woo Jin

    2018-03-01

    A novel prediction model for accurate determination of 5-year overall survival of gastric cancer patients was developed by an international collaborative group (G6+). This prediction model was created using a single institution's database of 11,851 Korean patients and included readily available and clinically relevant factors. Already validated using external East Asian cohorts, its applicability in the American population was yet to be determined. Using the Surveillance, Epidemiology, and End Results (SEER) dataset, 2014 release, all patients diagnosed with gastric adenocarcinoma who underwent surgical resection between 2002 and 2012, were selected. Characteristics for analysis included: age, sex, depth of tumor invasion, number of positive lymph nodes, total lymph nodes retrieved, presence of distant metastasis, extent of resection, and histology. Concordance index (C-statistic) was assessed using the novel prediction model and compared with the prognostic index, the seventh edition of the TNM staging system. Of the 26,019 gastric cancer patients identified from the SEER database, 15,483 had complete datasets. Validation of the novel prediction tool revealed a C-statistic of 0.762 (95% CI 0.754 to 0.769) compared with the seventh TNM staging model, C-statistic 0.683 (95% CI 0.677 to 0.689), (p prediction model for gastric cancer in the American patient population. Its superior prediction of the 5-year survival of gastric cancer patients in a large Western cohort strongly supports its global applicability. Importantly, this model allows for accurate prognosis for an increasing number of gastric cancer patients worldwide, including those who received inadequate lymphadenectomy or underwent a noncurative resection. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

  20. Predictive modeling of infrared radiative heating in tomato dry-peeling process: Part II. Model validation and sensitivity analysis

    Science.gov (United States)

    A predictive mathematical model was developed to simulate heat transfer in a tomato undergoing double sided infrared (IR) heating in a dry-peeling process. The aims of this study were to validate the developed model using experimental data and to investigate different engineering parameters that mos...

  1. A model to predict element redistribution in unsaturated soil: Its simplification and validation

    International Nuclear Information System (INIS)

    Sheppard, M.I.; Stephens, M.E.; Davis, P.A.; Wojciechowski, L.

    1991-01-01

    A research model has been developed to predict the long-term fate of contaminants entering unsaturated soil at the surface through irrigation or atmospheric deposition, and/or at the water table through groundwater. The model, called SCEMR1 (Soil Chemical Exchange and Migration of Radionuclides, Version 1), uses Darcy's law to model water movement, and the soil solid/liquid partition coefficient, K d , to model chemical exchange. SCEMR1 has been validated extensively on controlled field experiments with several soils, aeration statuses and the effects of plants. These validation results show that the model is robust and performs well. Sensitivity analyses identified soil K d , annual effective precipitation, soil type and soil depth to be the four most important model parameters. SCEMR1 consumes too much computer time for incorporation into a probabilistic assessment code. Therefore, we have used SCEMR1 output to derive a simple assessment model. The assessment model reflects the complexity of its parent code, and provides a more realistic description of containment transport in soils than would a compartment model. Comparison of the performance of the SCEMR1 research model, the simple SCEMR1 assessment model and the TERRA compartment model on a four-year soil-core experiment shows that the SCEMR1 assessment model generally provides conservative soil concentrations. (15 refs., 3 figs.)

  2. Validation of a risk prediction model for Barrett’s esophagus in an Australian population

    Directory of Open Access Journals (Sweden)

    Ireland CJ

    2018-03-01

    Full Text Available Colin J Ireland,1 Andrea L Gordon,2 Sarah K Thompson,3 David I Watson,4 David C Whiteman,5 Richard L Reed,6 Adrian Esterman1,7 1School of Nursing and Midwifery, Division of Health Sciences, University of South Australia, Adelaide, SA, Australia; 2School of Pharmacy and Medical Science, Division of Health Sciences, University of South Australia, Adelaide, SA, Australia; 3Discipline of Surgery, University of Adelaide, Adelaide, SA, Australia; 4Department of Surgery, Flinders University, Bedford Park, SA, Australia; 5Population Health Department, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia; 6Discipline of General Practice, Flinders University, Bedford Park, SA, Australia; 7Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, Australia Background: Esophageal adenocarcinoma is a disease that has a high mortality rate, the only known precursor being Barrett’s esophagus (BE. While screening for BE is not cost-effective at the population level, targeted screening might be beneficial. We have developed a risk prediction model to identify people with BE, and here we present the external validation of this model. Materials and methods: A cohort study was undertaken to validate a risk prediction model for BE. Individuals with endoscopy and histopathology proven BE completed a questionnaire containing variables previously identified as risk factors for this condition. Their responses were combined with data from a population sample for analysis. Risk scores were derived for each participant. Overall performance of the risk prediction model in terms of calibration and discrimination was assessed. Results: Scores from 95 individuals with BE and 636 individuals from the general population were analyzed. The Brier score was 0.118, suggesting reasonable overall performance. The area under the receiver operating characteristic was 0.83 (95% CI 0.78–0.87. The Hosmer–Lemeshow statistic was p=0

  3. External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

    Science.gov (United States)

    Devillers, J; Pandard, P; Richard, B

    2013-01-01

    Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

  4. Development and validation of the 3-D CFD model for CANDU-6 moderator temperature predictions

    International Nuclear Information System (INIS)

    Yoon, Churl; Rhee, Bo Wook; Min, Byung Joo

    2003-03-01

    A computational fluid dynamics model for predicting the moderator circulation inside the CANada Deuterium Uranium (CANDU) reactor vessel has been developed to estimate the local subcooling of the moderator in the vicinity of the Calandria tubes. The buoyancy effect induced by internal heating is accounted for by Boussinesq approximation. The standard κ-ε turbulence model associated with logarithmic wall treatment is applied to predict the turbulent jet flows from the inlet nozzles. The matrix of the Calandria tubes in the core region is simplified to porous media, in which an-isotropic hydraulic impedance is modeled using an empirical correlation of the frictional pressure loss. The governing equations are solved by CFX-4.4, a commercial CFD code developed by AEA technology. The CFD model has been successfully verified and validated against experimental data obtained in the Stern Laboratories Inc. (SLI) in Hamilton, Ontario

  5. A Validation of Subchannel Based CHF Prediction Model for Rod Bundles

    International Nuclear Information System (INIS)

    Hwang, Dae-Hyun; Kim, Seong-Jin

    2015-01-01

    A large number of CHF data base were procured from various sources which included square and non-square lattice test bundles. CHF prediction accuracy was evaluated for various models including CHF lookup table method, empirical correlations, and phenomenological DNB models. The parametric effect of the mass velocity and unheated wall has been investigated from the experimental result, and incorporated into the development of local parameter CHF correlation applicable to APWR conditions. According to the CHF design criterion, the CHF should not occur at the hottest rod in the reactor core during normal operation and anticipated operational occurrences with at least a 95% probability at a 95% confidence level. This is accomplished by assuring that the minimum DNBR (Departure from Nucleate Boiling Ratio) in the reactor core is greater than the limit DNBR which accounts for the accuracy of CHF prediction model. The limit DNBR can be determined from the inverse of the lower tolerance limit of M/P that is evaluated from the measured-to-predicted CHF ratios for the relevant CHF data base. It is important to evaluate an adequacy of the CHF prediction model for application to the actual reactor core conditions. Validation of CHF prediction model provides the degree of accuracy inferred from the comparison of solution and data. To achieve a required accuracy for the CHF prediction model, it may be necessary to calibrate the model parameters by employing the validation results. If the accuracy of the model is acceptable, then it is applied to the real complex system with the inferred accuracy of the model. In a conventional approach, the accuracy of CHF prediction model was evaluated from the M/P statistics for relevant CHF data base, which was evaluated by comparing the nominal values of the predicted and measured CHFs. The experimental uncertainty for the CHF data was not considered in this approach to determine the limit DNBR. When a subchannel based CHF prediction model

  6. Predicting medical complications after spine surgery: a validated model using a prospective surgical registry.

    Science.gov (United States)

    Lee, Michael J; Cizik, Amy M; Hamilton, Deven; Chapman, Jens R

    2014-02-01

    The possibility and likelihood of a postoperative medical complication after spine surgery undoubtedly play a major role in the decision making of the surgeon and patient alike. Although prior study has determined relative risk and odds ratio values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of medical complication, rather than relative risk or odds ratio values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. The purpose of this study was to create and validate a predictive model for the risk of medical complication during and after spine surgery. Statistical analysis using a prospective surgical spine registry that recorded extensive demographic, surgical, and complication data. Outcomes examined are medical complications that were specifically defined a priori. This analysis is a continuation of statistical analysis of our previously published report. Using a prospectively collected surgical registry of more than 1,476 patients with extensive demographic, comorbidity, surgical, and complication detail recorded for 2 years after surgery, we previously identified several risk factor for medical complications. Using the beta coefficients from those log binomial regression analyses, we created a model to predict the occurrence of medical complication after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created two predictive models: one predicting the occurrence of any medical complication and the other predicting the occurrence of a major medical complication. The final predictive model for any medical complications had a receiver operator curve characteristic of 0.76, considered to be a fair measure. The final predictive model for any major medical complications had

  7. Development and validation of multivariable predictive model for thromboembolic events in lymphoma patients.

    Science.gov (United States)

    Antic, Darko; Milic, Natasa; Nikolovski, Srdjan; Todorovic, Milena; Bila, Jelena; Djurdjevic, Predrag; Andjelic, Bosko; Djurasinovic, Vladislava; Sretenovic, Aleksandra; Vukovic, Vojin; Jelicic, Jelena; Hayman, Suzanne; Mihaljevic, Biljana

    2016-10-01

    Lymphoma patients are at increased risk of thromboembolic events but thromboprophylaxis in these patients is largely underused. We sought to develop and validate a simple model, based on individual clinical and laboratory patient characteristics that would designate lymphoma patients at risk for thromboembolic event. The study population included 1,820 lymphoma patients who were treated in the Lymphoma Departments at the Clinics of Hematology, Clinical Center of Serbia and Clinical Center Kragujevac. The model was developed using data from a derivation cohort (n = 1,236), and further assessed in the validation cohort (n = 584). Sixty-five patients (5.3%) in the derivation cohort and 34 (5.8%) patients in the validation cohort developed thromboembolic events. The variables independently associated with risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m(2) , reduced mobility, extranodal localization, development of neutropenia and hemoglobin level 3). For patients classified at risk (intermediate and high-risk scores), the model produced negative predictive value of 98.5%, positive predictive value of 25.1%, sensitivity of 75.4%, and specificity of 87.5%. A high-risk score had positive predictive value of 65.2%. The diagnostic performance measures retained similar values in the validation cohort. Developed prognostic Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis in cancer patients. Am. J. Hematol. 91:1014-1019, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  8. Anatomical Cystocele Recurrence: Development and Internal Validation of a Prediction Model.

    Science.gov (United States)

    Vergeldt, Tineke F M; van Kuijk, Sander M J; Notten, Kim J B; Kluivers, Kirsten B; Weemhoff, Mirjam

    2016-02-01

    To develop a prediction model that estimates the risk of anatomical cystocele recurrence after surgery. The databases of two multicenter prospective cohort studies were combined, and we performed a retrospective secondary analysis of these data. Women undergoing an anterior colporrhaphy without mesh materials and without previous pelvic organ prolapse (POP) surgery filled in a questionnaire, underwent translabial three-dimensional ultrasonography, and underwent staging of POP preoperatively and postoperatively. We developed a prediction model using multivariable logistic regression and internally validated it using standard bootstrapping techniques. The performance of the prediction model was assessed by computing indices of overall performance, discriminative ability, calibration, and its clinical utility by computing test characteristics. Of 287 included women, 149 (51.9%) had anatomical cystocele recurrence. Factors included in the prediction model were assisted delivery, preoperative cystocele stage, number of compartments involved, major levator ani muscle defects, and levator hiatal area during Valsalva. Potential predictors that were excluded after backward elimination because of high P values were age, body mass index, number of vaginal deliveries, and family history of POP. The shrinkage factor resulting from the bootstrap procedure was 0.91. After correction for optimism, Nagelkerke's R and the Brier score were 0.15 and 0.22, respectively. This indicates satisfactory model fit. The area under the receiver operating characteristic curve of the prediction model was 71.6% (95% confidence interval 65.7-77.5). After correction for optimism, the area under the receiver operating characteristic curve was 69.7%. This prediction model, including history of assisted delivery, preoperative stage, number of compartments, levator defects, and levator hiatus, estimates the risk of anatomical cystocele recurrence.

  9. Modelling for the Stripa site characterization and validation drift inflow: prediction of flow through fractured rock

    International Nuclear Information System (INIS)

    Herbert, A.; Gale, J.; MacLeod, R.; Lanyon, G.

    1991-12-01

    We present our approach to predicting flow through a fractured rock site; the site characterization and validation region in the Stripa mine. Our approach is based on discrete fracture network modelling using the NAPSAC computer code. We describe the conceptual models and assumptions that we have used to interpret the geometry and flow properties of the fracture networks, from measurements at the site. These are used to investigate large scale properties of the network and we show that for flows on scales larger than about 10 m, porous medium approximation should be used. The porous medium groundwater flow code CFEST is used to predict the large scale flows through the mine and the SCV region. This, in turn, is used to provide boundary conditions for more detailed models, which predict the details of flow, using a discrete fracture network model, on scales of less than 10 m. We conclude that a fracture network approach is feasible and that it provides a better understanding of details of flow than conventional porous medium approaches and a quantification of the uncertainty associated with predictive flow modelling characterised from field measurement in fractured rock. (au)

  10. A six-factor model of brand personality and its predictive validity

    Directory of Open Access Journals (Sweden)

    Živanović Marko

    2017-01-01

    Full Text Available The study examines applicability and usefulness of HEXACO-based model in the description of brand personality. Following contemporary theoretical developments in human personality research, Study 1 explored the latent personality structure of 120 brands using descriptors of six personality traits as defined in HEXACO model: Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness. The results of exploratory factor analyses have supported HEXACO personality six-factor structure to a large extent. In Study 2 we addressed the question of predictive validity of HEXACO-based brand personality. Brand personality traits, but predominantly Honesty-Humility, accounted for substantial amount of variance in prediction of important aspects of consumer-brand relationship: attitude toward brand, perceived quality of a brand, and brand loyalty. The implications of applying HEXACO-based brand personality in marketing research are discussed. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 179018 and Grant no. 175012

  11. Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

    Science.gov (United States)

    James, Matthew T; Pannu, Neesh; Hemmelgarn, Brenda R; Austin, Peter C; Tan, Zhi; McArthur, Eric; Manns, Braden J; Tonelli, Marcello; Wald, Ron; Quinn, Robert R; Ravani, Pietro; Garg, Amit X

    2017-11-14

    Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Demographic, laboratory, and comorbidity variables measured prior to discharge. Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher

  12. Validation of a CFD Analysis Model for Predicting CANDU-6 Moderator Temperature Against SPEL Experiments

    International Nuclear Information System (INIS)

    Churl Yoon; Bo Wook Rhee; Byung-Joo Min

    2002-01-01

    A validation of a 3D CFD model for predicting local subcooling of the moderator in the vicinity of calandria tubes in a CANDU-6 reactor is performed. The small scale moderator experiments performed at Sheridan Park Experimental Laboratory (SPEL) in Ontario, Canada[1] is used for the validation. Also a comparison is made between previous CFD analyses based on 2DMOTH and PHOENICS, and the current analysis for the same SPEL experiment. For the current model, a set of grid structures for the same geometry as the experimental test section is generated and the momentum, heat and continuity equations are solved by CFX-4.3, a CFD code developed by AEA technology. The matrix of calandria tubes is simplified by the porous media approach. The standard k-ε turbulence model associated with logarithmic wall treatment and SIMPLEC algorithm on the body fitted grid are used. Buoyancy effects are accounted for by the Boussinesq approximation. For the test conditions simulated in this study, the flow pattern identified is the buoyancy-dominated flow, which is generated by the interaction between the dominant buoyancy force by heating and inertial momentum forces by the inlet jets. As a result, the current CFD moderator analysis model predicts the moderator temperature reasonably, and the maximum error against the experimental data is kept at less than 2.0 deg. C over the whole domain. The simulated velocity field matches with the visualization of SPEL experiments quite well. (authors)

  13. Validated Loads Prediction Models for Offshore Wind Turbines for Enhanced Component Reliability

    DEFF Research Database (Denmark)

    Koukoura, Christina

    To improve the reliability of offshore wind turbines, accurate prediction of their response is required. Therefore, validation of models with site measurements is imperative. In the present thesis a 3.6MW pitch regulated-variable speed offshore wind turbine on a monopole foundation is built...... are used for the modification of the sub-structure/foundation design for possible material savings. First, the background of offshore wind engineering, including wind-wave conditions, support structure, blade loading and wind turbine dynamics are presented. Second, a detailed description of the site...

  14. Validation and prediction of traditional Chinese physical operation on spinal disease using multiple deformation models.

    Science.gov (United States)

    Pan, Lei; Yang, Xubo; Gu, Lixu; Lu, Wenlong; Fang, Min

    2011-03-01

    Traditional Chinese medical massage is a physical manipulation that achieves satisfactory results on spinal diseases, according to its advocates. However, the method relies on an expert's experience. Accurate analysis and simulation of massage are essential for validation of traditional Chinese physical treatment. The objective of this study is to provide analysis and simulation that can reproducibly verify and predict treatment efficacy. An improved physical multi-deformation model for simulating human cervical spine is proposed. First, the human spine, which includes muscle, vertebrae and inter- vertebral disks, are segmented and reconstructed from clinical CT and MR images. Homogeneous landmark registration is employed to align the spine models before and after the massage manipulation. Central line mass spring and contact FEM deformation models are used to individually evaluate spinal anatomy variations. The response of the human spine during the massage process is simulated based on specific clinical cases. Ten sets of patient data, including muscle-force relationships, displacement of vertebrae, strain and stress distribution on inter-vertebral disks were collected, including the pre-operation, post-operation and the 3-month follow-up. The simulation results demonstrate that traditional Chinese massage could significantly affect and treat most mild spinal disease. A new method that simulates a traditional Chinese medical massage operation on the human spine may be a useful tool to scientifically validate and predict treatment efficacy.

  15. A comprehensive model for the prediction of vibrations due to underground railway traffic: formulation and validation

    International Nuclear Information System (INIS)

    Costa, Pedro Alvares; Cardoso Silva, Antonio; Calçada, Rui; Lopes, Patricia; Fernandez, Jesus

    2016-01-01

    n this communication, a numerical approach for the prediction of vibrations induced in buildings due to railway traffic in tunnels is presented. The numerical model is based on the concept of dynamic sub structuring, being composed by three autonomous models to simulate the following main parts of the problem: i) generation of vibrations (train-track interaction); ii) propagation of vibrations (track - tunnel-ground system); iii) reception of vibrations (building coupled to the ground). The methodology proposed allows dealing with the three-dimensional characteristics of the problem with a reasonable computational effort [ 1 , 2 ] . After the brief description of the model, its experimental validation is performed. For that, a case study about vibrations inside of a building close to a shallow railway tunnel in Madrid are simulated and the experimental data [ 3 ] is compared with the predicted results [ 4 ]. Finally, the communication finishes with some insights about the potentialities and challenges of this numerical modelling approach on the prediction of the behavior of ancient structures subjected to vibrations induced by human sources (railway and road traffic, pile driving, etc)

  16. Readmissions and death after ICU discharge: development and validation of two predictive models.

    Directory of Open Access Journals (Sweden)

    Omar Badawi

    Full Text Available INTRODUCTION: Early discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS to assist ICU discharge planning. METHODS: Retrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011. EXCLUSION CRITERIA: DNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2∶1 to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge. RESULTS: 469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups. CONCLUSIONS: Our models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although

  17. Predicting the success of IVF: external validation of the van Loendersloot's model.

    Science.gov (United States)

    Sarais, Veronica; Reschini, Marco; Busnelli, Andrea; Biancardi, Rossella; Paffoni, Alessio; Somigliana, Edgardo

    2016-06-01

    Is the predictive model for IVF success proposed by van Loendersloot et al. valid in a different geographical and cultural context? The model discriminates well but was less accurate than in the original context where it was developed. Several independent groups have developed models that combine different variables with the aim of estimating the chance of pregnancy with IVF but only four of them have been externally validated. One of these four, the van Loendersloot's model, deserves particular attention and further investigation for at least three reasons; (i) the reported area under the receiver operating characteristics curve (c-statistics) in the temporal validation setting was the highest reported to date (0.68), (ii) the perspective of the model is clinically wise since it includes variables obtained from previous failed cycles, if any, so it can be applied to any women entering an IVF cycle, (iii) the model lacks external validation in a geographically different center. Retrospective cohort study of women undergoing oocyte retrieval for IVF between January 2013 and December 2013 at the infertility unit of the Fondazione Ca' Granda, Ospedale Maggiore Policlinico of Milan, Italy. Only the first oocyte retrieval cycle performed during the study period was included in the study. Women with previous IVF cycles were excluded if the last one before the study cycle was in another center. The main outcome was the cumulative live birth rate per oocytes retrieval. Seven hundred seventy-two women were selected. Variables included in the van Loendersloot's model and the relative weights (beta) were used. The variable resulting from this combination (Y) was transformed into a probability. The discriminatory capacity was assessed using the c-statistics. Calibration was made using a logistic regression that included Y as the unique variable and live birth as the outcome. Data are presented using both the original and the calibrated models. Performance was evaluated

  18. Development and Validation of Predictive Models of Cardiac Mortality and Transplantation in Resynchronization Therapy

    Directory of Open Access Journals (Sweden)

    Eduardo Arrais Rocha

    2015-01-01

    Full Text Available Abstract Background: 30-40% of cardiac resynchronization therapy cases do not achieve favorable outcomes. Objective: This study aimed to develop predictive models for the combined endpoint of cardiac death and transplantation (Tx at different stages of cardiac resynchronization therapy (CRT. Methods: Prospective observational study of 116 patients aged 64.8 ± 11.1 years, 68.1% of whom had functional class (FC III and 31.9% had ambulatory class IV. Clinical, electrocardiographic and echocardiographic variables were assessed by using Cox regression and Kaplan-Meier curves. Results: The cardiac mortality/Tx rate was 16.3% during the follow-up period of 34.0 ± 17.9 months. Prior to implantation, right ventricular dysfunction (RVD, ejection fraction < 25% and use of high doses of diuretics (HDD increased the risk of cardiac death and Tx by 3.9-, 4.8-, and 5.9-fold, respectively. In the first year after CRT, RVD, HDD and hospitalization due to congestive heart failure increased the risk of death at hazard ratios of 3.5, 5.3, and 12.5, respectively. In the second year after CRT, RVD and FC III/IV were significant risk factors of mortality in the multivariate Cox model. The accuracy rates of the models were 84.6% at preimplantation, 93% in the first year after CRT, and 90.5% in the second year after CRT. The models were validated by bootstrapping. Conclusion: We developed predictive models of cardiac death and Tx at different stages of CRT based on the analysis of simple and easily obtainable clinical and echocardiographic variables. The models showed good accuracy and adjustment, were validated internally, and are useful in the selection, monitoring and counseling of patients indicated for CRT.

  19. Factors associated with therapeutic inertia in hypertension: validation of a predictive model.

    Science.gov (United States)

    Redón, Josep; Coca, Antonio; Lázaro, Pablo; Aguilar, Ma Dolores; Cabañas, Mercedes; Gil, Natividad; Sánchez-Zamorano, Miguel Angel; Aranda, Pedro

    2010-08-01

    To study factors associated with therapeutic inertia in treating hypertension and to develop a predictive model to estimate the probability of therapeutic inertia in a given medical consultation, based on variables related to the consultation, patient, physician, clinical characteristics, and level of care. National, multicentre, observational, cross-sectional study in primary care and specialist (hospital) physicians who each completed a questionnaire on therapeutic inertia, provided professional data and collected clinical data on four patients. Therapeutic inertia was defined as a consultation in which treatment change was indicated (i.e., SBP >or= 140 or DBP >or= 90 mmHg in all patients; SBP >or= 130 or DBP >or= 80 in patients with diabetes or stroke), but did not occur. A predictive model was constructed and validated according to the factors associated with therapeutic inertia. Data were collected on 2595 patients and 13,792 visits. Therapeutic inertia occurred in 7546 (75%) of the 10,041 consultations in which treatment change was indicated. Factors associated with therapeutic inertia were primary care setting, male sex, older age, SPB and/or DBP values close to normal, treatment with more than one antihypertensive drug, treatment with an ARB II, and more than six visits/year. Physician characteristics did not weigh heavily in the association. The predictive model was valid internally and externally, with acceptable calibration, discrimination and reproducibility, and explained one-third of the variability in therapeutic inertia. Although therapeutic inertia is frequent in the management of hypertension, the factors explaining it are not completely clear. Whereas some aspects of the consultations were associated with therapeutic inertia, physician characteristics were not a decisive factor.

  20. Validation of Clinical Prediction Models: Theory and Applications in Testicular Germ Cell Cancer

    NARCIS (Netherlands)

    Y. Vergouwe (Yvonne)

    2003-01-01

    textabstractlinical prediction models combine patient characteristics to predict the probability of having a certain disease (diagnosis) or the probability that a particular disease state will occur (prognosis). The predicted probability of the diagnostic or prognostic outcome may assist the

  1. Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage : The SAHIT multinational cohort study

    NARCIS (Netherlands)

    Jaja, Blessing N R; Saposnik, Gustavo; Lingsma, Hester F.; Macdonald, Erin; Thorpe, Kevin E.; Mamdani, Muhammed; Steyerberg, Ewout W.; Molyneux, Andrew; Manoel, Airton Leonardo De Oliveira; Schatlo, Bawarjan; Hanggi, Daniel; Hasan, David M.; Wong, George K C; Etminan, Nima; Fukuda, Hitoshi; Torner, James C.; Schaller, Karl L.; Suarez, Jose I.; Stienen, Martin N.; Vergouwen, Mervyn D.I.; Rinkel, Gabriel J.E.; Spears, Julian; Cusimano, Michael D.; Todd, Michael; Le Roux, Peter; Kirkpatrick, Peter J.; Pickard, John; Van Den Bergh, Walter M.; Murray, Gordon D; Johnston, S. Claiborne; Yamagata, Sen; Mayer, Stephan A.; Schweizer, Tom A.; Macdonald, R. Loch

    2018-01-01

    Objective To develop and validate a set of practical prediction tools that reliably estimate the outcome of subarachnoid haemorrhage from ruptured intracranial aneurysms (SAH). Design Cohort study with logistic regression analysis to combine predictors and treatment modality. Setting Subarachnoid

  2. Validation of a Methodology to Predict Micro-Vibrations Based on Finite Element Model Approach

    Science.gov (United States)

    Soula, Laurent; Rathband, Ian; Laduree, Gregory

    2014-06-01

    This paper presents the second part of the ESA R&D study called "METhodology for Analysis of structure- borne MICro-vibrations" (METAMIC). After defining an integrated analysis and test methodology to help predicting micro-vibrations [1], a full-scale validation test campaign has been carried out. It is based on a bread-board representative of typical spacecraft (S/C) platform consisting in a versatile structure made of aluminium sandwich panels equipped with different disturbance sources and a dummy payload made of a silicon carbide (SiC) bench. The bread-board has been instrumented with a large set of sensitive accelerometers and tests have been performed including back-ground noise measurement, modal characterization and micro- vibration tests. The results provided responses to the perturbation coming from a reaction wheel or cryo-cooler compressors, operated independently then simultaneously with different operation modes. Using consistent modelling and associated experimental characterization techniques, a correlation status has been assessed by comparing test results with predictions based on FEM approach. Very good results have been achieved particularly for the case of a wheel in sweeping rate operation with test results over-predicted within a reasonable margin lower than two. Some limitations of the methodology have also been identified for sources operating at a fixed rate or coming with a small number of dominant harmonics and recommendations have been issued in order to deal with model uncertainties and stay conservative.

  3. Incremental validity of positive orientation: predictive efficiency beyond the five-factor model

    Directory of Open Access Journals (Sweden)

    Łukasz Roland Miciuk

    2016-05-01

    Full Text Available Background The relation of positive orientation (a basic predisposition to think positively of oneself, one’s life and one’s future and personality traits is still disputable. The purpose of the described research was to verify the hypothesis that positive orientation has predictive efficiency beyond the five-factor model. Participants and procedure One hundred and thirty participants (at the mean age M = 24.84 completed the following questionnaires: the Self-Esteem Scale (SES, the Satisfaction with Life Scale (SWLS, the Life Orientation Test-Revised (LOT-R, the Positivity Scale (P-SCALE, the NEO Five Factor Inventory (NEO-FFI, the Self-Concept Clarity Scale (SCC, the Generalized Self-Efficacy Scale (GSES and the Life Engagement Test (LET. Results The introduction of positive orientation as an additional predictor in the second step of regression analyses led to better prediction of the following variables: purpose in life, self-concept clarity and generalized self-efficacy. This effect was the strongest for predicting purpose in life (i.e. 14% increment of the explained variance. Conclusions The results confirmed our hypothesis that positive orientation can be characterized by incremental validity – its inclusion in the regression model (in addition to the five main factors of personality increases the amount of explained variance. These findings may provide further evidence for the legitimacy of measuring positive orientation and personality traits separately.

  4. Multivariable prediction model for suspected giant cell arteritis: development and validation

    Directory of Open Access Journals (Sweden)

    Ing EB

    2017-11-01

    Full Text Available Edsel B Ing,1 Gabriela Lahaie Luna,2 Andrew Toren,3 Royce Ing,4 John J Chen,5 Nitika Arora,6 Nurhan Torun,7 Otana A Jakpor,8 J Alexander Fraser,9 Felix J Tyndel,10 Arun NE Sundaram,10 Xinyang Liu,11 Cindy TY Lam,1 Vivek Patel,12 Ezekiel Weis,13 David Jordan,14 Steven Gilberg,14 Christian Pagnoux,15 Martin ten Hove21Department of Ophthalmology and Vision Sciences, University of Toronto Medical School, Toronto, 2Department of Ophthalmology, Queen’s University, Kingston, ON, 3Department of Ophthalmology, University of Laval, Quebec, QC, 4Toronto Eyelid, Strabismus and Orbit Surgery Clinic, Toronto, ON, Canada; 5Mayo Clinic, Department of Ophthalmology and Neurology, 6Mayo Clinic, Department of Ophthalmology, Rochester, MN, 7Department of Surgery, Division of Ophthalmology, Harvard Medical School, Boston, MA, 8Harvard Medical School, Boston, MA, USA; 9Department of Clinical Neurological Sciences and Ophthalmology, Western University, London, 10Department of Medicine, University of Toronto Medical School, Toronto, ON, Canada; 11Department of Medicine, Fudan University Shanghai Medical College, Shanghai, People’s Republic of China; 12Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; 13Departments of Ophthalmology, Universities of Alberta and Calgary, Edmonton and Calgary, AB, 14Department of Ophthalmology, University of Ottawa, Ottawa, ON, 15Vasculitis Clinic, Mount Sinai Hospital, Toronto, ON, CanadaPurpose: To develop and validate a diagnostic prediction model for patients with suspected giant cell arteritis (GCA.Methods: A retrospective review of records of consecutive adult patients undergoing temporal artery biopsy (TABx for suspected GCA was conducted at seven university centers. The pathologic diagnosis was considered the final diagnosis. The predictor variables were age, gender, new onset headache, clinical temporal artery abnormality, jaw claudication, ischemic vision loss (VL, diplopia

  5. Validation of a predictive model for smart control of electrical energy storage

    NARCIS (Netherlands)

    Homan, Bart; van Leeuwen, Richard Pieter; Smit, Gerardus Johannes Maria; Zhu, Lei; de Wit, Jan B.

    2016-01-01

    The purpose of this paper is to investigate the applicability of a relatively simple model which is based on energy conservation for model predictions as part of smart control of thermal and electric storage. The paper reviews commonly used predictive models. Model predictions of charging and

  6. Predicting plant invasions under climate change: are species distribution models validated by field trials?

    Science.gov (United States)

    Sheppard, Christine S; Burns, Bruce R; Stanley, Margaret C

    2014-09-01

    Climate change may facilitate alien species invasion into new areas, particularly for species from warm native ranges introduced into areas currently marginal for temperature. Although conclusions from modelling approaches and experimental studies are generally similar, combining the two approaches has rarely occurred. The aim of this study was to validate species distribution models by conducting field trials in sites of differing suitability as predicted by the models, thus increasing confidence in their ability to assess invasion risk. Three recently naturalized alien plants in New Zealand were used as study species (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla): they originate from warm native ranges, are woody bird-dispersed species and of concern as potential weeds. Seedlings were grown in six sites across the country, differing both in climate and suitability (as predicted by the species distribution models). Seedling growth and survival were recorded over two summers and one or two winter seasons, and temperature and precipitation were monitored hourly at each site. Additionally, alien seedling performances were compared to those of closely related native species (Rhopalostylis sapida, Lophomyrtus bullata and Schefflera digitata). Furthermore, half of the seedlings were sprayed with pesticide, to investigate whether enemy release may influence performance. The results showed large differences in growth and survival of the alien species among the six sites. In the more suitable sites, performance was frequently higher compared to the native species. Leaf damage from invertebrate herbivory was low for both alien and native seedlings, with little evidence that the alien species should have an advantage over the native species because of enemy release. Correlations between performance in the field and predicted suitability of species distribution models were generally high. The projected increase in minimum temperature and reduced

  7. Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma

    International Nuclear Information System (INIS)

    Zhao, Hui; Hua, Ye; Dai, Tu; He, Jian; Tang, Min; Fu, Xu; Mao, Liang; Jin, Huihan; Qiu, Yudong

    2017-01-01

    Highlights: • This study aimed to establish a novel predictive scoring model of MVI in HCC patients. • Preoperative imaging features on CECT, such as intratumoral arteries, non-nodule type and absence of radiological tumor capsule were independent predictors for MVI. • The predictive scoring model is of great value in prediction of MVI regardless of tumor size. - Abstract: Purpose: Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion. Methods: A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n = 206) and validation cohort (n = 103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts. Results: Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5 cm and >5 cm in AUROC (P = 0.910). Conclusions: The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI

  8. Development and validation of a novel predictive scoring model for microvascular invasion in patients with hepatocellular carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Hui [Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu (China); Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second People' s Hospital, Wuxi, Jiangsu (China); Hua, Ye [Department of Neurology, Nanjing Medical University Affiliated Wuxi Second People’s Hospital, Wuxi, Jiangsu (China); Dai, Tu [Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second People' s Hospital, Wuxi, Jiangsu (China); He, Jian; Tang, Min [Department of Radiology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu (China); Fu, Xu; Mao, Liang [Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu (China); Jin, Huihan, E-mail: 45687061@qq.com [Department of Hepatopancreatobiliary Surgery, Nanjing Medical University Affiliated Wuxi Second People' s Hospital, Wuxi, Jiangsu (China); Qiu, Yudong, E-mail: yudongqiu510@163.com [Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu (China)

    2017-03-15

    Highlights: • This study aimed to establish a novel predictive scoring model of MVI in HCC patients. • Preoperative imaging features on CECT, such as intratumoral arteries, non-nodule type and absence of radiological tumor capsule were independent predictors for MVI. • The predictive scoring model is of great value in prediction of MVI regardless of tumor size. - Abstract: Purpose: Microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) cannot be accurately predicted preoperatively. This study aimed to establish a predictive scoring model of MVI in solitary HCC patients without macroscopic vascular invasion. Methods: A total of 309 consecutive HCC patients who underwent curative hepatectomy were divided into the derivation (n = 206) and validation cohort (n = 103). A predictive scoring model of MVI was established according to the valuable predictors in the derivation cohort based on multivariate logistic regression analysis. The performance of the predictive model was evaluated in the derivation and validation cohorts. Results: Preoperative imaging features on CECT, such as intratumoral arteries, non-nodular type of HCC and absence of radiological tumor capsule were independent predictors for MVI. The predictive scoring model was established according to the β coefficients of the 3 predictors. Area under receiver operating characteristic (AUROC) of the predictive scoring model was 0.872 (95% CI, 0.817-0.928) and 0.856 (95% CI, 0.771-0.940) in the derivation and validation cohorts. The positive and negative predictive values were 76.5% and 88.0% in the derivation cohort and 74.4% and 88.3% in the validation cohort. The performance of the model was similar between the patients with tumor size ≤5 cm and >5 cm in AUROC (P = 0.910). Conclusions: The predictive scoring model based on intratumoral arteries, non-nodular type of HCC, and absence of the radiological tumor capsule on preoperative CECT is of great value in the prediction of MVI

  9. Validation and Refinement of Prediction Models to Estimate Exercise Capacity in Cancer Survivors Using the Steep Ramp Test

    NARCIS (Netherlands)

    Stuiver, Martijn M.; Kampshoff, Caroline S.; Persoon, Saskia; Groen, Wim; van Mechelen, Willem; Chinapaw, Mai J. M.; Brug, Johannes; Nollet, Frans; Kersten, Marie-José; Schep, Goof; Buffart, Laurien M.

    2017-01-01

    Objective: To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo2(peak)) and peak power output (W-peak).&

  10. Development and validation of a CFD model predicting the backfill process of a nuclear waste gallery

    International Nuclear Information System (INIS)

    Gopala, Vinay Ramohalli; Lycklama a Nijeholt, Jan-Aiso; Bakker, Paul; Haverkate, Benno

    2011-01-01

    Research highlights: → This work presents the CFD simulation of the backfill process of Supercontainers with nuclear waste emplaced in a disposal gallery. → The cement-based material used for backfill is grout and the flow of grout is modelled as a Bingham fluid. → The model is verified against an analytical solution and validated against the flowability tests for concrete. → Comparison between backfill plexiglas experiment and simulation shows a distinct difference in the filling pattern. → The numerical model needs to be further developed to include segregation effects and thixotropic behavior of grout. - Abstract: Nuclear waste material may be stored in underground tunnels for long term storage. The example treated in this article is based on the current Belgian disposal concept for High-Level Waste (HLW), in which the nuclear waste material is packed in concrete shielded packages, called Supercontainers, which are inserted into these tunnels. After placement of the packages in the underground tunnels, the remaining voids between the packages and the tunnel lining is filled-up with a cement-based material called grout in order to encase the stored containers into the underground spacing. This encasement of the stored containers inside the tunnels is known as the backfill process. A good backfill process is necessary to stabilize the waste gallery against ground settlements. A numerical model to simulate the backfill process can help to improve and optimize the process by ensuring a homogeneous filling with no air voids and also optimization of the injection positions to achieve a homogeneous filling. The objective of the present work is to develop such a numerical code that can predict the backfill process well and validate the model against the available experiments and analytical solutions. In the present work the rheology of Grout is modelled as a Bingham fluid which is implemented in OpenFOAM - a finite volume-based open source computational fluid

  11. External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome.

    Science.gov (United States)

    Zhao, Zhiguo; Wickersham, Nancy; Kangelaris, Kirsten N; May, Addison K; Bernard, Gordon R; Matthay, Michael A; Calfee, Carolyn S; Koyama, Tatsuki; Ware, Lorraine B

    2017-08-01

    Mortality prediction in ARDS is important for prognostication and risk stratification. However, no prediction models have been independently validated. A combination of two biomarkers with age and APACHE III was superior in predicting mortality in the NHLBI ARDSNet ALVEOLI trial. We validated this prediction tool in two clinical trials and an observational cohort. The validation cohorts included 849 patients from the NHLBI ARDSNet Fluid and Catheter Treatment Trial (FACTT), 144 patients from a clinical trial of sivelestat for ARDS (STRIVE), and 545 ARDS patients from the VALID observational cohort study. To evaluate the performance of the prediction model, the area under the receiver operating characteristic curve (AUC), model discrimination, and calibration were assessed, and recalibration methods were applied. The biomarker/clinical prediction model performed well in all cohorts. Performance was better in the clinical trials with an AUC of 0.74 (95% CI 0.70-0.79) in FACTT, compared to 0.72 (95% CI 0.67-0.77) in VALID, a more heterogeneous observational cohort. The AUC was 0.73 (95% CI 0.70-0.76) when FACTT and VALID were combined. We validated a mortality prediction model for ARDS that includes age, APACHE III, surfactant protein D, and interleukin-8 in a variety of clinical settings. Although the model performance as measured by AUC was lower than in the original model derivation cohort, the biomarker/clinical model still performed well and may be useful for risk assessment for clinical trial enrollment, an issue of increasing importance as ARDS mortality declines, and better methods are needed for selection of the most severely ill patients for inclusion.

  12. Developing and validating a model to predict the success of an IHCS implementation: the Readiness for Implementation Model

    Science.gov (United States)

    Gustafson, David H; Hawkins, Robert P; Brennan, Patricia F; Dinauer, Susan; Johnson, Pauley R; Siegler, Tracy

    2010-01-01

    Objective To develop and validate the Readiness for Implementation Model (RIM). This model predicts a healthcare organization's potential for success in implementing an interactive health communication system (IHCS). The model consists of seven weighted factors, with each factor containing five to seven elements. Design Two decision-analytic approaches, self-explicated and conjoint analysis, were used to measure the weights of the RIM with a sample of 410 experts. The RIM model with weights was then validated in a prospective study of 25 IHCS implementation cases. Measurements Orthogonal main effects design was used to develop 700 conjoint-analysis profiles, which varied on seven factors. Each of the 410 experts rated the importance and desirability of the factors and their levels, as well as a set of 10 different profiles. For the prospective 25-case validation, three time-repeated measures of the RIM scores were collected for comparison with the implementation outcomes. Results Two of the seven factors, ‘organizational motivation’ and ‘meeting user needs,’ were found to be most important in predicting implementation readiness. No statistically significant difference was found in the predictive validity of the two approaches (self-explicated and conjoint analysis). The RIM was a better predictor for the 1-year implementation outcome than the half-year outcome. Limitations The expert sample, the order of the survey tasks, the additive model, and basing the RIM cut-off score on experience are possible limitations of the study. Conclusion The RIM needs to be empirically evaluated in institutions adopting IHCS and sustaining the system in the long term. PMID:20962135

  13. Validation of a multi-marker model for the prediction of incident type 2 diabetes mellitus

    DEFF Research Database (Denmark)

    Lyssenko, Valeriya; Jørgensen, Torben; Gerwien, Robert W

    2012-01-01

    Purpose: To assess performance of a biomarker-based score that predicts the five-year risk of diabetes (Diabetes Risk Score, DRS) in an independent cohort that included 15-year follow-up. Method: DRS was developed on the Inter99 cohort, and validated on the Botnia cohort. Performance...... was benchmarked against other risk-assessment tools comparing calibration, time to event analysis, and net reclassification. Results: The area under the receiver-operating characteristic curve (AUC) was 0.84 for the Inter99 cohort and 0.78 for the Botnia cohort. In the Botnia cohort, DRS provided better...... discrimination than fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance, oral glucose tolerance test or risk scores derived from Framingham or San Antonio Study cohorts. Overall reclassification with DRS was significantly better than using FPG and glucose tolerance status (p

  14. Methodology for experimental validation of a CFD model for predicting noise generation in centrifugal compressors

    International Nuclear Information System (INIS)

    Broatch, A.; Galindo, J.; Navarro, R.; García-Tíscar, J.

    2014-01-01

    Highlights: • A DES of a turbocharger compressor working at peak pressure point is performed. • In-duct pressure signals are measured in a steady flow rig with 3-sensor arrays. • Pressure spectra comparison is performed as a validation for the numerical model. • A suitable comparison methodology is developed, relying on pressure decomposition. • Whoosh noise at outlet duct is detected in experimental and numerical spectra. - Abstract: Centrifugal compressors working in the surge side of the map generate a broadband noise in the range of 1–3 kHz, named as whoosh noise. This noise is perceived at strongly downsized engines operating at particular conditions (full load, tip-in and tip-out maneuvers). A 3-dimensional CFD model of a centrifugal compressor is built to analyze fluid phenomena related to whoosh noise. A detached eddy simulation is performed with the compressor operating at the peak pressure point of 160 krpm. A steady flow rig mounted on an anechoic chamber is used to obtain experimental measurements as a means of validation for the numerical model. In-duct pressure signals are obtained in addition to standard averaged global variables. The numerical simulation provides global variables showing excellent agreement with experimental measurements. Pressure spectra comparison is performed to assess noise prediction capability of numerical model. The influence of the type and position of the virtual pressure probes is evaluated. Pressure decomposition is required by the simulations to obtain meaningful spectra. Different techniques for obtaining pressure components are analyzed. At the simulated conditions, a broadband noise in 1–3 kHz frequency band is detected in the experimental measurements. This whoosh noise is also captured by the numerical model

  15. Development and validation of prediction models for endometrial cancer in postmenopausal bleeding.

    Science.gov (United States)

    Wong, Alyssa Sze-Wai; Cheung, Chun Wai; Fung, Linda Wen-Ying; Lao, Terence Tzu-Hsi; Mol, Ben Willem J; Sahota, Daljit Singh

    2016-08-01

    To develop and assess the accuracy of risk prediction models to diagnose endometrial cancer in women having postmenopausal bleeding (PMB). A retrospective cohort study of 4383 women in a One-stop PMB clinic from a university teaching hospital in Hong Kong. Clinical risk factors, transvaginal ultrasonic measurement of endometrial thickness (ET) and endometrial histology were obtained from consecutive women between 2002 and 2013. Two models to predict risk of endometrial cancer were developed and assessed, one based on patient characteristics alone and a second incorporated ET with patient characteristics. Endometrial histology was used as the reference standard. The split-sample internal validation and bootstrapping technique were adopted. The optimal threshold for prediction of endometrial cancer by the final models was determined using a receiver-operating characteristics (ROC) curve and Youden Index. The diagnostic gain was compared to a reference strategy of measuring ET only by comparing the AUC using the Delong test. Out of 4383 women with PMB, 168 (3.8%) were diagnosed with endometrial cancer. ET alone had an area under curve (AUC) of 0.92 (95% confidence intervals [CIs] 0.89-0.94). In the patient characteristics only model, independent predictors of cancer were age at presentation, age at menopause, body mass index, nulliparity and recurrent vaginal bleeding. The AUC and Youdens Index of the patient characteristic only model were respectively 0.73 (95% CI 0.67-0.80) and 0.72 (Sensitivity=66.5%; Specificity=68.9%; +ve LR=2.14; -ve LR=0.49). ET, age at presentation, nulliparity and recurrent vaginal bleeding were independent predictors in the patient characteristics plus ET model. The AUC and Youdens Index of the patient characteristic plus ET model where respectively 0.92 (95% CI 0.88-0.96) and 0.71 (Sensitivity=82.7%; Specificity=88.3%; +ve LR=6.38; -ve LR=0.2). Comparison of AUC indicated that a history alone model was inferior to a model using ET alone

  16. On various metrics used for validation of predictive QSAR models with applications in virtual screening and focused library design.

    Science.gov (United States)

    Roy, Kunal; Mitra, Indrani

    2011-07-01

    Quantitative structure-activity relationships (QSARs) have important applications in drug discovery research, environmental fate modeling, property prediction, etc. Validation has been recognized as a very important step for QSAR model development. As one of the important objectives of QSAR modeling is to predict activity/property/toxicity of new chemicals falling within the domain of applicability of the developed models and QSARs are being used for regulatory decisions, checking reliability of the models and confidence of their predictions is a very important aspect, which can be judged during the validation process. One prime application of a statistically significant QSAR model is virtual screening for molecules with improved potency based on the pharmacophoric features and the descriptors appearing in the QSAR model. Validated QSAR models may also be utilized for design of focused libraries which may be subsequently screened for the selection of hits. The present review focuses on various metrics used for validation of predictive QSAR models together with an overview of the application of QSAR models in the fields of virtual screening and focused library design for diverse series of compounds with citation of some recent examples.

  17. A Validated Analytical Model for Availability Prediction of IPTV Services in VANETs

    Directory of Open Access Journals (Sweden)

    Bernd E. Wolfinger

    2014-12-01

    Full Text Available In vehicular ad hoc networks (VANETs, besides the original applications typically related to traffic safety, we nowadays can observe an increasing trend toward infotainment applications, such as IPTV services. Quality of experience (QoE, as observed by the end users of IPTV, is highly important to guarantee adequate user acceptance for the service. In IPTV, QoE is mainly determined by the availability of TV channels for the users. This paper presents an efficient and rather generally applicable analytical model that allows one to predict the blocking probability of TV channels, both for channel-switching-induced, as well as for handover-induced blocking events. We present the successful validation of the model by means of simulation, and we introduce a new measure for QoE. Numerous case studies illustrate how the analytical model and our new QoE measure can be applied successfully for the dimensioning of IPTV systems, taking into account the QoE requirements of the IPTV service users in strongly diverse traffic scenarios.

  18. Validation of a Predictive Model for Survival in Metastatic Cancer Patients Attending an Outpatient Palliative Radiotherapy Clinic

    International Nuclear Information System (INIS)

    Chow, Edward; Abdolell, Mohamed; Panzarella, Tony; Harris, Kristin; Bezjak, Andrea; Warde, Padraig; Tannock, Ian

    2009-01-01

    Purpose: To validate a predictive model for survival of patients attending a palliative radiotherapy clinic. Methods and Materials: We described previously a model that had good predictive value for survival of patients referred during 1999 (1). The six prognostic factors (primary cancer site, site of metastases, Karnofsky performance score, and the fatigue, appetite and shortness-of-breath items from the Edmonton Symptom Assessment Scale) identified in this training set were extracted from the prospective database for the year 2000. We generated a partial score whereby each prognostic factor was assigned a value proportional to its prognostic weight. The sum of the partial scores for each patient was used to construct a survival prediction score (SPS). Patients were also grouped according to the number of these risk factors (NRF) that they possessed. The probability of survival at 3, 6, and 12 months was generated. The models were evaluated for their ability to predict survival in this validation set with appropriate statistical tests. Results: The median survival and survival probabilities of the training and validation sets were similar when separated into three groups using both SPS and NRF methods. There was no statistical difference in the performance of the SPS and NRF methods in survival prediction. Conclusion: Both the SPS and NRF models for predicting survival in patients referred for palliative radiotherapy have been validated. The NRF model is preferred because it is simpler and avoids the need to remember the weightings among the prognostic factors

  19. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Science.gov (United States)

    King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

    2011-01-01

    Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  20. Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study.

    Directory of Open Access Journals (Sweden)

    Michael King

    Full Text Available Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL for the development of hazardous drinking in safe drinkers.A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873. The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51. External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846 and Hedge's g of 0.68 (95% CI 0.57, 0.78.The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

  1. Performance prediction and validation of equilibrium modeling for gasification of cashew nut shell char

    Directory of Open Access Journals (Sweden)

    M. Venkata Ramanan

    2008-09-01

    Full Text Available Cashew nut shell, a waste product obtained during deshelling of cashew kernels, had in the past been deemed unfit as a fuel for gasification owing to its high occluded oil content. The oil, a source of natural phenol, oozes upon gasification, thereby clogging the gasifier throat, downstream equipment and associated utilities with oil, resulting in ineffective gasification and premature failure of utilities due to its corrosive characteristics. To overcome this drawback, the cashew shells were de-oiled by charring in closed chambers and were subsequently gasified in an autothermal downdraft gasifier. Equilibrium modeling was carried out to predict the producer gas composition under varying performance influencing parameters, viz., equivalence ratio (ER, reaction temperature (RT and moisture content (MC. The results were compared with the experimental output and are presented in this paper. The model is quite satisfactory with the experimental outcome at the ER applicable to gasification systems, i.e., 0.15 to 0.30. The results show that the mole fraction of (i H2, CO and CH4 decreases while (N2 + H2O and CO2 increases with ER, (ii H2 and CO increases while CH4, (N2 + H2O and CO2 decreases with reaction temperature, (iii H2, CH4, CO2 and (N2 + H2O increases while CO decreases with moisture content. However at an equivalence ratio less than 0.15, the model predicts an unrealistic composition and is observed to be non valid below this ER.

  2. An efficient numerical target strength prediction model: Validation against analysis solutions

    NARCIS (Netherlands)

    Fillinger, L.; Nijhof, M.J.J.; Jong, C.A.F. de

    2014-01-01

    A decade ago, TNO developed RASP (Rapid Acoustic Signature Prediction), a numerical model for the prediction of the target strength of immersed underwater objects. The model is based on Kirchhoff diffraction theory. It is currently being improved to model refraction, angle dependent reflection and

  3. Predictive validity of the Hendrich fall risk model II in an acute geriatric unit.

    Science.gov (United States)

    Ivziku, Dhurata; Matarese, Maria; Pedone, Claudio

    2011-04-01

    Falls are the most common adverse events reported in acute care hospitals, and older patients are the most likely to fall. The risk of falling cannot be completely eliminated, but it can be reduced through the implementation of a fall prevention program. A major evidence-based intervention to prevent falls has been the use of fall-risk assessment tools. Many tools have been increasingly developed in recent years, but most instruments have not been investigated regarding reliability, validity and clinical usefulness. This study intends to evaluate the predictive validity and inter-rater reliability of Hendrich fall risk model II (HFRM II) in order to identify older patients at risk of falling in geriatric units and recommend its use in clinical practice. A prospective descriptive design was used. The study was carried out in a geriatric acute care unit of an Italian University hospital. All over 65 years old patients consecutively admitted to a geriatric acute care unit of an Italian University hospital over 8-month period were enrolled. The patients enrolled were screened for the falls risk by nurses with the HFRM II within 24h of admission. The falls occurring during the patient's hospital stay were registered. Inter-rater reliability, area under the ROC curve, sensitivity, specificity, positive and negative predictive values and time for the administration were evaluated. 179 elderly patients were included. The inter-rater reliability was 0.87 (95% CI 0.71-1.00). The administration time was about 1min. The most frequently reported risk factors were depression, incontinence, vertigo. Sensitivity and specificity were respectively 86% and 43%. The optimal cut-off score for screening at risk patients was 5 with an area under the ROC curve of 0.72. The risk factors more strongly associated with falls were confusion and depression. As falls of older patients are a common problem in acute care settings it is necessary that the nurses use specific validate and reliable

  4. Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer

    NARCIS (Netherlands)

    Petersen, Japke F.; Stuiver, Martijn M.; Timmermans, Adriana J.; Chen, Amy; Zhang, Hongzhen; O'Neill, James P.; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T.; Koch, Wayne; van den Brekel, Michiel W. M.

    2017-01-01

    TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442

  5. A systematic approach to obtain validated Partial Least Square models for predicting lipoprotein subclasses from serum NMR spectra

    NARCIS (Netherlands)

    Mihaleva, V.V.; van Schalkwijk, D.B.; de Graaf, A.A.; van Duynhoven, J.; van Dorsten, F.A.; Vervoort, J.; Smilde, A.; Westerhuis, J.A.; Jacobs, D.M.

    2014-01-01

    A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited 1H NMR spectra and calibrated on

  6. A systematic approach to obtain validated partial least square models for predicting lipoprotein subclasses from serum NMR spectra

    NARCIS (Netherlands)

    Mihaleva, V.V.; Schalkwijk, van D.B.; Graaf, de A.A.; Duynhoven, van J.P.M.; Dorsten, van F.A.; Vervoort, J.J.M.; Smilde, A.K.; Westerhuis, J.A.; Jacobs, D.M.

    2014-01-01

    A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited (1)H NMR spectra and calibrated on

  7. A systematic approach to obtain validated partial least square models for predicting lipoprotein subclasses from serum nmr spectra

    NARCIS (Netherlands)

    Mihaleva, V.V.; Schalkwijk, D.B. van; Graaf, A.A. de; Duynhoven, J. van; Dorsten, F.A. van; Vervoort, J.; Smilde, A.; Westerhuis, J.A.; Jacobs, D.M.

    2014-01-01

    A systematic approach is described for building validated PLS models that predict cholesterol and triglyceride concentrations in lipoprotein subclasses in fasting serum from a normolipidemic, healthy population. The PLS models were built on diffusion-edited 1H NMR spectra and calibrated on

  8. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Stapelfeldt, Christina M.; Heymans, Martijn W.; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V.; Bultmann, Ute; Jensen, Chris

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. Methods 2,562 municipal eldercare

  9. Determining the validity of exposure models for environmental epidemiology : predicting electromagnetic fields from mobile phone base stations

    NARCIS (Netherlands)

    Beekhuizen, Johan|info:eu-repo/dai/nl/34472641X

    2014-01-01

    One of the key challenges in environmental epidemiology is the exposure assessment of large populations. Spatial exposure models have been developed that predict exposure to the pollutant of interest for large study sizes. However, the validity of these exposure models is often unknown. In this

  10. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

    Energy Technology Data Exchange (ETDEWEB)

    Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov [Science and Research Staff, Office of Pharmaceutical Science, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993–0002 (United States); Cross, Kevin P. [Leadscope, Inc., 1393 Dublin Road, Columbus, OH, 43215–1084 (United States)

    2012-05-01

    Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive

  11. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

    International Nuclear Information System (INIS)

    Valerio, Luis G.; Cross, Kevin P.

    2012-01-01

    Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describe the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.

  12. External validation of models predicting the individual risk of metachronous peritoneal carcinomatosis from colon and rectal cancer.

    Science.gov (United States)

    Segelman, J; Akre, O; Gustafsson, U O; Bottai, M; Martling, A

    2016-04-01

    To externally validate previously published predictive models of the risk of developing metachronous peritoneal carcinomatosis (PC) after resection of nonmetastatic colon or rectal cancer and to update the predictive model for colon cancer by adding new prognostic predictors. Data from all patients with Stage I-III colorectal cancer identified from a population-based database in Stockholm between 2008 and 2010 were used. We assessed the concordance between the predicted and observed probabilities of PC and utilized proportional-hazard regression to update the predictive model for colon cancer. When applied to the new validation dataset (n = 2011), the colon and rectal cancer risk-score models predicted metachronous PC with a concordance index of 79% and 67%, respectively. After adding the subclasses of pT3 and pT4 stage and mucinous tumour to the colon cancer model, the concordance index increased to 82%. In validation of external and recent cohorts, the predictive accuracy was strong in colon cancer and moderate in rectal cancer patients. The model can be used to identify high-risk patients for planned second-look laparoscopy/laparotomy for possible subsequent cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

  13. Exacerbations in adults with asthma: A systematic review and external validation of prediction models

    NARCIS (Netherlands)

    Loymans, Rik J. B.; Debray, Thomas P. A.; Honkoop, Persijn J.; Termeer, Evelien H.; Snoeck-Stroband, Jiska B.; Schermer, Tjard R. J.; Assendelft, Willem J. J.; Timp, Merel; Chung, Kian Fan; Sousa, Ana R.; Sont, Jaap K.; Sterk, Peter J.; Reddel, Helen K.; ter Riet, Gerben

    2018-01-01

    Several prediction models assessing future risk of exacerbations in adult patients with asthma have been published. Applicability of these models is uncertain because their predictive performance has often not been assessed beyond the population in which they were derived. This study aimed to

  14. Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients

    International Nuclear Information System (INIS)

    Dehing-Oberije, Cary; De Ruysscher, Dirk; Petit, Steven; Van Meerbeeck, Jan; Vandecasteele, Katrien; De Neve, Wilfried; Dingemans, Anne Marie C.; El Naqa, Issam; Deasy, Joseph; Bradley, Jeff; Huang, Ellen; Lambin, Philippe

    2010-01-01

    Introduction: Acute dysphagia is a distressing dose-limiting toxicity occurring frequently during concurrent chemo-radiation or high-dose radiotherapy for lung cancer. It can lead to treatment interruptions and thus jeopardize survival. Although a number of predictive factors have been identified, it is still not clear how these could offer assistance for treatment decision making in daily clinical practice. Therefore, we have developed and validated a nomogram to predict this side-effect. In addition, clinical usefulness was assessed by comparing model predictions to physicians' predictions. Materials and methods: Clinical data from 469 inoperable lung cancer patients, treated with curative intent, were collected prospectively. A prediction model for acute radiation-induced dysphagia was developed. Model performance was evaluated by the c-statistic and assessed using bootstrapping as well as two external datasets. In addition, a prospective study was conducted comparing model to physicians' predictions in 138 patients. Results: The final multivariate model consisted of age, gender, WHO performance status, mean esophageal dose (MED), maximum esophageal dose (MAXED) and overall treatment time (OTT). The c-statistic, assessed by bootstrapping, was 0.77. External validation yielded an AUC of 0.94 on the Ghent data and 0.77 on the Washington University St. Louis data for dysphagia ≥ grade 3. Comparing model predictions to the physicians' predictions resulted in an AUC of 0.75 versus 0.53, respectively. Conclusions: The proposed model performed well was successfully validated and demonstrated the ability to predict acute severe dysphagia remarkably better than the physicians. Therefore, this model could be used in clinical practice to identify patients at high or low risk.

  15. Predicting Environmental Suitability for a Rare and Threatened Species (Lao Newt, Laotriton laoensis) Using Validated Species Distribution Models

    Science.gov (United States)

    Chunco, Amanda J.; Phimmachak, Somphouthone; Sivongxay, Niane; Stuart, Bryan L.

    2013-01-01

    The Lao newt (Laotriton laoensis) is a recently described species currently known only from northern Laos. Little is known about the species, but it is threatened as a result of overharvesting. We integrated field survey results with climate and altitude data to predict the geographic distribution of this species using the niche modeling program Maxent, and we validated these predictions by using interviews with local residents to confirm model predictions of presence and absence. The results of the validated Maxent models were then used to characterize the environmental conditions of areas predicted suitable for L. laoensis. Finally, we overlaid the resulting model with a map of current national protected areas in Laos to determine whether or not any land predicted to be suitable for this species is coincident with a national protected area. We found that both area under the curve (AUC) values and interview data provided strong support for the predictive power of these models, and we suggest that interview data could be used more widely in species distribution niche modeling. Our results further indicated that this species is mostly likely geographically restricted to high altitude regions (i.e., over 1,000 m elevation) in northern Laos and that only a minute fraction of suitable habitat is currently protected. This work thus emphasizes that increased protection efforts, including listing this species as endangered and the establishment of protected areas in the region predicted to be suitable for L. laoensis, are urgently needed. PMID:23555808

  16. HEDR model validation plan

    International Nuclear Information System (INIS)

    Napier, B.A.; Gilbert, R.O.; Simpson, J.C.; Ramsdell, J.V. Jr.; Thiede, M.E.; Walters, W.H.

    1993-06-01

    The Hanford Environmental Dose Reconstruction (HEDR) Project has developed a set of computational ''tools'' for estimating the possible radiation dose that individuals may have received from past Hanford Site operations. This document describes the planned activities to ''validate'' these tools. In the sense of the HEDR Project, ''validation'' is a process carried out by comparing computational model predictions with field observations and experimental measurements that are independent of those used to develop the model

  17. Cross-National Validation of Prognostic Models Predicting Sickness Absence and the Added Value of Work Environment Variables

    NARCIS (Netherlands)

    Roelen, C.A.M.; Stapelfeldt, C.M.; Heijmans, M.W.; van Rhenen, W.; Labriola, M.; Nielsen, C.V.; Bultmann, U.; Jensen, C.

    2015-01-01

    Purpose To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models’ risk discrimination was also investigated. Methods 2,562 municipal eldercare

  18. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables

    NARCIS (Netherlands)

    Roelen, Corne; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bultmann, Ute; Bjorner, Jakob

    2018-01-01

    Purpose: The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Materials and methods: Based on the literature, 15 predictor

  19. Prediction model of RSV-hospitalization in late preterm infants : An update and validation study

    NARCIS (Netherlands)

    Korsten, Koos; Blanken, Maarten O; Nibbelke, Elisabeth E; Moons, Karel G M; Bont, Louis

    BACKGROUND: New vaccines and RSV therapeutics have been developed in the past decade. With approval of these new pharmaceuticals on the horizon, new challenges lie ahead in selecting the appropriate target population. We aimed to improve a previously published prediction model for prediction of

  20. Prediction model of RSV-hospitalization in late preterm infants: An update and validation study

    NARCIS (Netherlands)

    Korsten, K.; Blanken, M.O.; Nibbelke, E.E.; Moons, K.G.; Bont, L.; Liem, K.D.; et al.,

    2016-01-01

    BACKGROUND: New vaccines and RSV therapeutics have been developed in the past decade. With approval of these new pharmaceuticals on the horizon, new challenges lie ahead in selecting the appropriate target population. We aimed to improve a previously published prediction model for prediction of

  1. Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies.

    Science.gov (United States)

    Amin, Sk Abdul; Adhikari, Nilanjan; Baidya, Sandip Kumar; Gayen, Shovanlal; Jha, Tarun

    2018-01-03

    Chemokines trigger numerous inflammatory responses and modulate the immune system. The interaction between monocyte chemoattractant protein-1 and chemokine receptor 2 (CCR2) may be the cause of atherosclerosis, obesity, and insulin resistance. However, CCR2 is also implicated in other inflammatory diseases such as rheumatoid arthritis, multiple sclerosis, asthma, and neuropathic pain. Therefore, there is a paramount importance of designing potent and selective CCR2 antagonists despite a number of drug candidates failed in clinical trials. In this article, 83 CCR2 antagonists by Jhonson and Jhonson Pharmaceuticals have been considered for robust validated multi-QSAR modeling studies to get an idea about the structural and pharmacophoric requirements for designing more potent CCR2 antagonists. All these QSAR models were validated and statistically reliable. Observations resulted from different modeling studies correlated and validated results of other ones. Finally, depending on these QSAR observations, some new molecules were proposed that may exhibit higher activity against CCR2.

  2. Predictive validity of endpoints used in electrophysiological modelling of migraine in the trigeminovascular system.

    Science.gov (United States)

    Farkas, Bence; Kardos, Péter; Orosz, Szabolcs; Tarnawa, István; Csekő, Csongor; Lévay, György; Farkas, Sándor; Lendvai, Balázs; Kovács, Péter

    2015-11-02

    The trigeminovascular system has a pivotal role in the pathomechanism of migraine. The aim of the present study was to further develop existing models of migraine making them more suitable for testing the effects of compounds with presumed antimigraine activity in anaesthetised rats. Simultaneous recording of ongoing activity of spontaneously active neurons in the trigeminocervical complex as well as their discharges evoked by electrical stimulation of the dura mater via activation of A- and C-sensory fibres were carried out. Effects of sumatriptan, propranolol and topiramate were evaluated prior to and after application of a mixture containing inflammatory mediators on the dura. Propranolol (10 mg/kg s.c) and topiramate (30 mg/kg s.c.) resulted in a tendency to decrease the level of both spontaneous and evoked activity, while sumatriptan (1 mg/kg s.c.) did not exhibit any effect on recorded parameters. Application of an inflammatory soup to the dura mater boosted up spontaneous activity, which could be significantly attenuated by propranolol and topiramate but not by sumatriptan. In addition, all compounds prevented the delayed increase of spontaneous firing. In contrast to the ongoing activity, evoked responses were not augmented by inflammatory mediators. Nevertheless, inhibitory effect of propranolol and topiramate was evident when considering A- or C-fibre responses. Findings do not support the view that electrically evoked responses are useful for the measurement of trigeminal sensitization. It is proposed however, that inhibition of enhanced firing (immediate and/or delayed) evoked by inflammatory mediators as an endpoint have higher predictive validity regarding the clinical effectiveness of compounds. Copyright © 2015 Elsevier B.V. All rights reserved.

  3. Validation of Energy Expenditure Prediction Models Using Real-Time Shoe-Based Motion Detectors.

    Science.gov (United States)

    Lin, Shih-Yun; Lai, Ying-Chih; Hsia, Chi-Chun; Su, Pei-Fang; Chang, Chih-Han

    2017-09-01

    This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers. Three physical activity (PA) datasets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The "unclassified" dataset was defined as the data without PA recognition, the "recognition" as the data classified with PA recognition, and the "intensity segmentation" as the data with intensity segmentation. The three datasets contained accelerometer signals (quantified as signal magnitude area (SMA)) and net heart rate (HR net ). The accuracy of these models was assessed according to the deviation between physically measured EE and model-estimated EE. The variance between physically measured EE and model-estimated EE expressed by simple linear regressions was increased by 63% and 13% using SMA and HR net , respectively. The accuracy of the EE predicted from accelerometer signals is influenced by the different activities that exhibit different count-EE relationships within the same prediction model. The recognition model provides a better estimation and lower variability of EE compared with the unclassified and intensity segmentation models. The proposed shoe-based motion detectors can improve the accuracy of EE estimation and has great potential to be used to manage everyday exercise in real time.

  4. Developing and validating a new precise risk-prediction model for new-onset hypertension: The Jichi Genki hypertension prediction model (JG model).

    Science.gov (United States)

    Kanegae, Hiroshi; Oikawa, Takamitsu; Suzuki, Kenji; Okawara, Yukie; Kario, Kazuomi

    2018-03-31

    No integrated risk assessment tools that include lifestyle factors and uric acid have been developed. In accordance with the Industrial Safety and Health Law in Japan, a follow-up examination of 63 495 normotensive individuals (mean age 42.8 years) who underwent a health checkup in 2010 was conducted every year for 5 years. The primary endpoint was new-onset hypertension (systolic blood pressure [SBP]/diastolic blood pressure [DBP] ≥ 140/90 mm Hg and/or the initiation of antihypertensive medications with self-reported hypertension). During the mean 3.4 years of follow-up, 7402 participants (11.7%) developed hypertension. The prediction model included age, sex, body mass index (BMI), SBP, DBP, low-density lipoprotein cholesterol, uric acid, proteinuria, current smoking, alcohol intake, eating rate, DBP by age, and BMI by age at baseline and was created by using Cox proportional hazards models to calculate 3-year absolute risks. The derivation analysis confirmed that the model performed well both with respect to discrimination and calibration (n = 63 495; C-statistic = 0.885, 95% confidence interval [CI], 0.865-0.903; χ 2 statistic = 13.6, degree of freedom [df] = 7). In the external validation analysis, moreover, the model performed well both in its discrimination and calibration characteristics (n = 14 168; C-statistic = 0.846; 95%CI, 0.775-0.905; χ 2 statistic = 8.7, df = 7). Adding LDL cholesterol, uric acid, proteinuria, alcohol intake, eating rate, and BMI by age to the base model yielded a significantly higher C-statistic, net reclassification improvement (NRI), and integrated discrimination improvement, especially NRI non-event (NRI = 0.127, 95%CI = 0.100-0.152; NRI non-event  = 0.108, 95%CI = 0.102-0.117). In conclusion, a highly precise model with good performance was developed for predicting incident hypertension using the new parameters of eating rate, uric acid, proteinuria, and BMI by age. ©2018 Wiley Periodicals, Inc.

  5. Prediction of dissolved reactive phosphorus losses from small agricultural catchments: calibration and validation of a parsimonious model

    Directory of Open Access Journals (Sweden)

    C. Hahn

    2013-10-01

    Full Text Available Eutrophication of surface waters due to diffuse phosphorus (P losses continues to be a severe water quality problem worldwide, causing the loss of ecosystem functions of the respective water bodies. Phosphorus in runoff often originates from a small fraction of a catchment only. Targeting mitigation measures to these critical source areas (CSAs is expected to be most efficient and cost-effective, but requires suitable tools. Here we investigated the capability of the parsimonious Rainfall-Runoff-Phosphorus (RRP model to identify CSAs in grassland-dominated catchments based on readily available soil and topographic data. After simultaneous calibration on runoff data from four small hilly catchments on the Swiss Plateau, the model was validated on a different catchment in the same region without further calibration. The RRP model adequately simulated the discharge and dissolved reactive P (DRP export from the validation catchment. Sensitivity analysis showed that the model predictions were robust with respect to the classification of soils into "poorly drained" and "well drained", based on the available soil map. Comparing spatial hydrological model predictions with field data from the validation catchment provided further evidence that the assumptions underlying the model are valid and that the model adequately accounts for the dominant P export processes in the target region. Thus, the parsimonious RRP model is a valuable tool that can be used to determine CSAs. Despite the considerable predictive uncertainty regarding the spatial extent of CSAs, the RRP can provide guidance for the implementation of mitigation measures. The model helps to identify those parts of a catchment where high DRP losses are expected or can be excluded with high confidence. Legacy P was predicted to be the dominant source for DRP losses and thus, in combination with hydrologic active areas, a high risk for water quality.

  6. A Trap Motion in Validating Muscle Activity Prediction from Musculoskeletal Model using EMG

    NARCIS (Netherlands)

    Wibawa, A. D.; Verdonschot, N.; Halbertsma, J.P.K.; Burgerhof, J.G.M.; Diercks, R.L.; Verkerke, G. J.

    2016-01-01

    Musculoskeletal modeling nowadays is becoming the most common tool for studying and analyzing human motion. Besides its potential in predicting muscle activity and muscle force during active motion, musculoskeletal modeling can also calculate many important kinetic data that are difficult to measure

  7. Validation and Refinement of Prediction Models to Estimate Exercise Capacity in Cancer Survivors Using the Steep Ramp Test.

    Science.gov (United States)

    Stuiver, Martijn M; Kampshoff, Caroline S; Persoon, Saskia; Groen, Wim; van Mechelen, Willem; Chinapaw, Mai J M; Brug, Johannes; Nollet, Frans; Kersten, Marie-José; Schep, Goof; Buffart, Laurien M

    2017-11-01

    To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo 2peak ) and peak power output (W peak ). Cross-sectional study. Multicenter. Cancer survivors (N=283) in 2 randomized controlled exercise trials. Not applicable. Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions. ICCs of SRT-predicted Vo 2peak and W peak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for Vo 2peak and ±59W with a bias of 5W for W peak . Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of Vo 2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of W peak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for W peak . Predictions of Vo 2peak and W peak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for W peak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  8. Summary of best guidelines and validation of CFD modeling in livestock buildings to ensure prediction quality

    DEFF Research Database (Denmark)

    Rong, Li; Nielsen, Peter Vilhelm; Bjerg, Bjarne Schmidt

    2016-01-01

    scale pig barns was simulated to show the procedures of validating a CFD simulation in livestock buildings. After summarizing the guideline and/or best practice for CFD modeling, the authors addressed the issues related to numerical methods and the governing equations, which were limited to RANS models....... Although it is not necessary to maintain the same format of reporting the CFD modeling as presented in this paper, the authors would suggest including all the information related to the selection of turbulence models, difference schemes, convergence criteria, boundary conditions, geometry simplification......, simulating domain etc. This information is particularly important for the readers to evaluate the quality of the CFD simulation results....

  9. A Multivariate Model for Prediction of Obstructive Coronary Disease in Patients with Acute Chest Pain: Development and Validation

    Directory of Open Access Journals (Sweden)

    Luis Cláudio Lemos Correia

    Full Text Available Abstract Background: Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective: To develop and validate a multivariate model to predict coronary artery disease (CAD based on variables assessed at admission to the coronary care unit (CCU due to acute chest pain. Methods: A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%, while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results: The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84 in the derivation sample and 0.86 (95%CI = 0.79 - 0.93 in the validation sample. Hosmer-Lemeshow's test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively. Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002 and validation (p = 0.039 samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001 and validation (p < 0.0015 samples. Conclusion: A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain.

  10. External Validation Study of First Trimester Obstetric Prediction Models (Expect Study I): Research Protocol and Population Characteristics.

    Science.gov (United States)

    Meertens, Linda Jacqueline Elisabeth; Scheepers, Hubertina Cj; De Vries, Raymond G; Dirksen, Carmen D; Korstjens, Irene; Mulder, Antonius Lm; Nieuwenhuijze, Marianne J; Nijhuis, Jan G; Spaanderman, Marc Ea; Smits, Luc Jm

    2017-10-26

    A number of first-trimester prediction models addressing important obstetric outcomes have been published. However, most models have not been externally validated. External validation is essential before implementing a prediction model in clinical practice. The objective of this paper is to describe the design of a study to externally validate existing first trimester obstetric prediction models, based upon maternal characteristics and standard measurements (eg, blood pressure), for the risk of pre-eclampsia (PE), gestational diabetes mellitus (GDM), spontaneous preterm birth (PTB), small-for-gestational-age (SGA) infants, and large-for-gestational-age (LGA) infants among Dutch pregnant women (Expect Study I). The results of a pilot study on the feasibility and acceptability of the recruitment process and the comprehensibility of the Pregnancy Questionnaire 1 are also reported. A multicenter prospective cohort study was performed in The Netherlands between July 1, 2013 and December 31, 2015. First trimester obstetric prediction models were systematically selected from the literature. Predictor variables were measured by the Web-based Pregnancy Questionnaire 1 and pregnancy outcomes were established using the Postpartum Questionnaire 1 and medical records. Information about maternal health-related quality of life, costs, and satisfaction with Dutch obstetric care was collected from a subsample of women. A pilot study was carried out before the official start of inclusion. External validity of the models will be evaluated by assessing discrimination and calibration. Based on the pilot study, minor improvements were made to the recruitment process and online Pregnancy Questionnaire 1. The validation cohort consists of 2614 women. Data analysis of the external validation study is in progress. This study will offer insight into the generalizability of existing, non-invasive first trimester prediction models for various obstetric outcomes in a Dutch obstetric population

  11. The diagnostic value of specific IgE to Ara h 2 to predict peanut allergy in children is comparable to a validated and updated diagnostic prediction model.

    Science.gov (United States)

    Klemans, Rob J B; Otte, Dianne; Knol, Mirjam; Knol, Edward F; Meijer, Yolanda; Gmelig-Meyling, Frits H J; Bruijnzeel-Koomen, Carla A F M; Knulst, André C; Pasmans, Suzanne G M A

    2013-01-01

    A diagnostic prediction model for peanut allergy in children was recently published, using 6 predictors: sex, age, history, skin prick test, peanut specific immunoglobulin E (sIgE), and total IgE minus peanut sIgE. To validate this model and update it by adding allergic rhinitis, atopic dermatitis, and sIgE to peanut components Ara h 1, 2, 3, and 8 as candidate predictors. To develop a new model based only on sIgE to peanut components. Validation was performed by testing discrimination (diagnostic value) with an area under the receiver operating characteristic curve and calibration (agreement between predicted and observed frequencies of peanut allergy) with the Hosmer-Lemeshow test and a calibration plot. The performance of the (updated) models was similarly analyzed. Validation of the model in 100 patients showed good discrimination (88%) but poor calibration (P original model: sex, skin prick test, peanut sIgE, and total IgE minus sIgE. When building a model with sIgE to peanut components, Ara h 2 was the only predictor, with a discriminative ability of 90%. Cutoff values with 100% positive and negative predictive values could be calculated for both the updated model and sIgE to Ara h 2. In this way, the outcome of the food challenge could be predicted with 100% accuracy in 59% (updated model) and 50% (Ara h 2) of the patients. Discrimination of the validated model was good; however, calibration was poor. The discriminative ability of Ara h 2 was almost comparable to that of the updated model, containing 4 predictors. With both models, the need for peanut challenges could be reduced by at least 50%. Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.

  12. Acute Kidney Injury in Trauma Patients Admitted to Critical Care: Development and Validation of a Diagnostic Prediction Model.

    Science.gov (United States)

    Haines, Ryan W; Lin, Shih-Pin; Hewson, Russell; Kirwan, Christopher J; Torrance, Hew D; O'Dwyer, Michael J; West, Anita; Brohi, Karim; Pearse, Rupert M; Zolfaghari, Parjam; Prowle, John R

    2018-02-26

    Acute Kidney Injury (AKI) complicating major trauma is associated with increased mortality and morbidity. Traumatic AKI has specific risk factors and predictable time-course facilitating diagnostic modelling. In a single centre, retrospective observational study we developed risk prediction models for AKI after trauma based on data around intensive care admission. Models predicting AKI were developed using data from 830 patients, using data reduction followed by logistic regression, and were independently validated in a further 564 patients. AKI occurred in 163/830 (19.6%) with 42 (5.1%) receiving renal replacement therapy (RRT). First serum creatinine and phosphate, units of blood transfused in first 24 h, age and Charlson score discriminated need for RRT and AKI early after trauma. For RRT c-statistics were good to excellent: development: 0.92 (0.88-0.96), validation: 0.91 (0.86-0.97). Modelling AKI stage 2-3, c-statistics were also good, development: 0.81 (0.75-0.88) and validation: 0.83 (0.74-0.92). The model predicting AKI stage 1-3 performed moderately, development: c-statistic 0.77 (0.72-0.81), validation: 0.70 (0.64-0.77). Despite good discrimination of need for RRT, positive predictive values (PPV) at the optimal cut-off were only 23.0% (13.7-42.7) in development. However, PPV for the alternative endpoint of RRT and/or death improved to 41.2% (34.8-48.1) highlighting death as a clinically relevant endpoint to RRT.

  13. Development and validation of a multivariate prediction model for patients with acute pancreatitis in Intensive Care Medicine.

    Science.gov (United States)

    Zubia-Olaskoaga, Felix; Maraví-Poma, Enrique; Urreta-Barallobre, Iratxe; Ramírez-Puerta, María-Rosario; Mourelo-Fariña, Mónica; Marcos-Neira, María-Pilar; García-García, Miguel Ángel

    2018-03-01

    Development and validation of a multivariate prediction model for patients with acute pancreatitis (AP) admitted in Intensive Care Units (ICU). A prospective multicenter observational study, in 1 year period, in 46 international ICUs (EPAMI study). adults admitted to an ICU with AP and at least one organ failure. Development of a multivariate prediction model, using the worst data of the stay in ICU, based in multivariate analysis, simple imputation in a development cohort. The model was validated in another cohort. 374 patients were included (mortality of 28.9%). Variables with statistical significance in multivariate analysis were age, no alcoholic and no biliary etiology, development of shock, development of respiratory failure, need of continuous renal replacement therapy, and intra-abdominal pressure. The model created with these variables presented an AUC of ROC curve of 0.90 (CI 95% 0.81-0.94) in the validation cohort. We developed a multivariable prediction model, and AP cases could be classified as low mortality risk (between 2 and 9.5 points, mortality of 1.35%), moderate mortality risk (between 10 and 12.5 points, 28.92% of mortality), and high mortality risk (13 points of more, mortality of 88.37%). Our model presented better AUC of ROC curve than APACHE II (0.91 vs 0.80) and SOFA in the first 24 h (0.91 vs 0.79). We developed and validated a multivariate prediction model, which can be applied in any moment of the stay in ICU, with better discriminatory power than APACHE II and SOFA in the first 24 h. Copyright © 2018 IAP and EPC. Published by Elsevier B.V. All rights reserved.

  14. Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

    Science.gov (United States)

    Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

    2015-06-01

    To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

  15. Experimental Validation of Surrogate Models for Predicting the Draping of Physical Interpolating Surfaces

    DEFF Research Database (Denmark)

    Christensen, Esben Toke; Lund, Erik; Lindgaard, Esben

    2018-01-01

    This paper concerns the experimental validation of two surrogate models through a benchmark study involving two different variable shape mould prototype systems. The surrogate models in question are different methods based on kriging and proper orthogonal decomposition (POD), which were developed...... to the performance of the studied surrogate models. By comparing surrogate model performance for the two variable shape mould systems, and through a numerical study involving simple finite element models, the underlying cause of this effect is explained. It is concluded that for a variable shape mould prototype...... hypercube approach. This sampling method allows for generating a space filling and high-quality sample plan that respects mechanical constraints of the variable shape mould systems. Through the benchmark study, it is found that mechanical freeplay in the modeled system is severely detrimental...

  16. Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields.

    Science.gov (United States)

    Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin; Strawn, Laura K

    2016-02-01

    Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

  17. Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

    Science.gov (United States)

    Weller, Daniel; Shiwakoti, Suvash; Bergholz, Peter; Grohn, Yrjo; Wiedmann, Martin

    2015-01-01

    Technological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n = 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce. PMID:26590280

  18. Modelling sexual transmission of HIV: testing the assumptions, validating the predictions

    Science.gov (United States)

    Baggaley, Rebecca F.; Fraser, Christophe

    2010-01-01

    Purpose of review To discuss the role of mathematical models of sexual transmission of HIV: the methods used and their impact. Recent findings We use mathematical modelling of “universal test and treat” as a case study to illustrate wider issues relevant to all modelling of sexual HIV transmission. Summary Mathematical models are used extensively in HIV epidemiology to deduce the logical conclusions arising from one or more sets of assumptions. Simple models lead to broad qualitative understanding, while complex models can encode more realistic assumptions and thus be used for predictive or operational purposes. An overreliance on model analysis where assumptions are untested and input parameters cannot be estimated should be avoided. Simple models providing bold assertions have provided compelling arguments in recent public health policy, but may not adequately reflect the uncertainty inherent in the analysis. PMID:20543600

  19. Validation of Quantitative Structure-Activity Relationship (QSAR Model for Photosensitizer Activity Prediction

    Directory of Open Access Journals (Sweden)

    Sharifuddin M. Zain

    2011-11-01

    Full Text Available Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA method. Based on the method, r2 value, r2 (CV value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 µM to 7.04 µM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.

  20. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables.

    Science.gov (United States)

    Roelen, Corné; Thorsen, Sannie; Heymans, Martijn; Twisk, Jos; Bültmann, Ute; Bjørner, Jakob

    2018-01-01

    The purpose of this study is to develop and validate a prediction model for identifying employees at increased risk of long-term sickness absence (LTSA), by using variables commonly measured in occupational health surveys. Based on the literature, 15 predictor variables were retrieved from the DAnish National working Environment Survey (DANES) and included in a model predicting incident LTSA (≥4 consecutive weeks) during 1-year follow-up in a sample of 4000 DANES participants. The 15-predictor model was reduced by backward stepwise statistical techniques and then validated in a sample of 2524 DANES participants, not included in the development sample. Identification of employees at increased LTSA risk was investigated by receiver operating characteristic (ROC) analysis; the area-under-the-ROC-curve (AUC) reflected discrimination between employees with and without LTSA during follow-up. The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC = 0.68; 95% CI 0.61-0.76), but not practically useful. A prediction model based on occupational health survey variables identified employees with an increased LTSA risk, but should be further developed into a practically useful tool to predict the risk of LTSA in the general working population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for

  1. Development and validation of a prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.

    Science.gov (United States)

    Mathioudakis, Nestoras Nicolas; Everett, Estelle; Routh, Shuvodra; Pronovost, Peter J; Yeh, Hsin-Chieh; Golden, Sherita Hill; Saria, Suchi

    2018-01-01

    To develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults. We collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and model development and validation, respectively. Using predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CV BG ), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (-LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CV BG , diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and -LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia. Hospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.

  2. Development of a new model to predict indoor daylighting: Integration in CODYRUN software and validation

    Energy Technology Data Exchange (ETDEWEB)

    Fakra, A.H., E-mail: fakra@univ-reunion.f [Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT), University of La Reunion, 117 rue du General Ailleret, 97430 Le Tampon (French Overseas Dpt.), Reunion (France); Miranville, F.; Boyer, H.; Guichard, S. [Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT), University of La Reunion, 117 rue du General Ailleret, 97430 Le Tampon (French Overseas Dpt.), Reunion (France)

    2011-07-15

    Research highlights: {yields} This study presents a new model capable to simulate indoor daylighting. {yields} The model was introduced in research software called CODYRUN. {yields} The validation of the code was realized from a lot of tests cases. -- Abstract: Many models exist in the scientific literature for determining indoor daylighting values. They are classified in three categories: numerical, simplified and empirical models. Nevertheless, each of these categories of models are not convenient for every application. Indeed, the numerical model requires high calculation time; conditions of use of the simplified models are limited, and experimental models need not only important financial resources but also a perfect control of experimental devices (e.g. scale model), as well as climatic characteristics of the location (e.g. in situ experiment). In this article, a new model based on a combination of multiple simplified models is established. The objective is to improve this category of model. The originality of our paper relies on the coupling of several simplified models of indoor daylighting calculations. The accuracy of the simulation code, introduced into CODYRUN software to simulate correctly indoor illuminance, is then verified. Besides, the software consists of a numerical building simulation code, developed in the Physics and Mathematical Engineering Laboratory for Energy and Environment (PIMENT) at the University of Reunion. Initially dedicated to the thermal, airflow and hydrous phenomena in the buildings, the software has been completed for the calculation of indoor daylighting. New models and algorithms - which rely on a semi-detailed approach - will be presented in this paper. In order to validate the accuracy of the integrated models, many test cases have been considered as analytical, inter-software comparisons and experimental comparisons. In order to prove the accuracy of the new model - which can properly simulate the illuminance - a

  3. Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

    Directory of Open Access Journals (Sweden)

    Meyfroidt Geert

    2011-10-01

    Full Text Available Abstract Background The intensive care unit (ICU length of stay (LOS of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP, a machine learning technique. Methods Non-interventional study. Predictive modeling, separate development (n = 461 and validation (n = 499 cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task, and to predict the day of ICU discharge as a discrete variable (regression task. GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF ((actual-predicted/actual and calculating root mean squared relative errors (RMSRE. Results Median (P25-P75 ICU length of stay was 3 (2-5 days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%, which was significantly better than the EuroSCORE (p Conclusions A GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a

  4. Development and validation of clinical prediction models for mortality, functional outcome and cognitive impairment after stroke: a study protocol.

    Science.gov (United States)

    Fahey, Marion; Rudd, Anthony; Béjot, Yannick; Wolfe, Charles; Douiri, Abdel

    2017-08-18

    Stroke is a leading cause of adult disability and death worldwide. The neurological impairments associated with stroke prevent patients from performing basic daily activities and have enormous impact on families and caregivers. Practical and accurate tools to assist in predicting outcome after stroke at patient level can provide significant aid for patient management. Furthermore, prediction models of this kind can be useful for clinical research, health economics, policymaking and clinical decision support. 2869 patients with first-ever stroke from South London Stroke Register (SLSR) (1995-2004) will be included in the development cohort. We will use information captured after baseline to construct multilevel models and a Cox proportional hazard model to predict cognitive impairment, functional outcome and mortality up to 5 years after stroke. Repeated random subsampling validation (Monte Carlo cross-validation) will be evaluated in model development. Data from participants recruited to the stroke register (2005-2014) will be used for temporal validation of the models. Data from participants recruited to the Dijon Stroke Register (1985-2015) will be used for external validation. Discrimination, calibration and clinical utility of the models will be presented. Patients, or for patients who cannot consent their relatives, gave written informed consent to participate in stroke-related studies within the SLSR. The SLSR design was approved by the ethics committees of Guy's and St Thomas' NHS Foundation Trust, Kings College Hospital, Queens Square and Westminster Hospitals (London). The Dijon Stroke Registry was approved by the Comité National des Registres and the InVS and has authorisation of the Commission Nationale de l'Informatique et des Libertés. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

  5. Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.).

    Science.gov (United States)

    Akbar, Abdul; Kuanar, Ananya; Joshi, Raj K; Sandeep, I S; Mohanty, Sujata; Naik, Pradeep K; Mishra, Antaryami; Nayak, Sanghamitra

    2016-01-01

    The drug yielding potential of turmeric ( Curcuma longa L.) is largely due to the presence of phyto-constituent 'curcumin.' Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R 2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.

  6. Development of prediction model and experimental validation in predicting the curcumin content of turmeric (Curcuma longa L.

    Directory of Open Access Journals (Sweden)

    Abdul Akbar

    2016-10-01

    Full Text Available The drug yielding potential of turmeric (Curcuma longa L. is largely due to the presence of phyto-constituent ‘curcumin’. Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8 was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.

  7. Development and validation of a prediction model for tube feeding dependence after curative (chemo- radiation in head and neck cancer.

    Directory of Open Access Journals (Sweden)

    Kim Wopken

    Full Text Available BACKGROUND: Curative radiotherapy or chemoradiation for head and neck cancer (HNC may result in severe acute and late side effects, including tube feeding dependence. The purpose of this prospective cohort study was to develop a prediction model for tube feeding dependence 6 months (TUBEM6 after curative (chemo- radiotherapy in HNC patients. PATIENTS AND METHODS: Tube feeding dependence was scored prospectively. To develop the multivariable model, a group LASSO analysis was carried out, with TUBEM6 as the primary endpoint (n = 427. The model was then validated in a test cohort (n = 183. The training cohort was divided into three groups based on the risk of TUBEM6 to test whether the model could be extrapolated to later time points (12, 18 and 24 months. RESULTS: Most important predictors for TUBEM6 were weight loss prior to treatment, advanced T-stage, positive N-stage, bilateral neck irradiation, accelerated radiotherapy and chemoradiation. Model performance was good, with an Area under the Curve of 0.86 in the training cohort and 0.82 in the test cohort. The TUBEM6-based risk groups were significantly associated with tube feeding dependence at later time points (p<0.001. CONCLUSION: We established an externally validated predictive model for tube feeding dependence after curative radiotherapy or chemoradiation, which can be used to predict TUBEM6.

  8. Boolean Dynamic Modeling Approaches to Study Plant Gene Regulatory Networks: Integration, Validation, and Prediction.

    Science.gov (United States)

    Velderraín, José Dávila; Martínez-García, Juan Carlos; Álvarez-Buylla, Elena R

    2017-01-01

    Mathematical models based on dynamical systems theory are well-suited tools for the integration of available molecular experimental data into coherent frameworks in order to propose hypotheses about the cooperative regulatory mechanisms driving developmental processes. Computational analysis of the proposed models using well-established methods enables testing the hypotheses by contrasting predictions with observations. Within such framework, Boolean gene regulatory network dynamical models have been extensively used in modeling plant development. Boolean models are simple and intuitively appealing, ideal tools for collaborative efforts between theorists and experimentalists. In this chapter we present protocols used in our group for the study of diverse plant developmental processes. We focus on conceptual clarity and practical implementation, providing directions to the corresponding technical literature.

  9. SU-E-T-479: Development and Validation of Analytical Models Predicting Secondary Neutron Radiation in Proton Therapy Applications

    International Nuclear Information System (INIS)

    Farah, J; Bonfrate, A; Donadille, L; Martinetti, F; Trompier, F; Clairand, I; De Olivera, A; Delacroix, S; Herault, J; Piau, S; Vabre, I

    2014-01-01

    Purpose: Test and validation of analytical models predicting leakage neutron exposure in passively scattered proton therapy. Methods: Taking inspiration from the literature, this work attempts to build an analytical model predicting neutron ambient dose equivalents, H*(10), within the local 75 MeV ocular proton therapy facility. MC simulations were first used to model H*(10) in the beam axis plane while considering a closed final collimator and pristine Bragg peak delivery. Next, MC-based analytical model was tested against simulation results and experimental measurements. The model was also expended in the vertical direction to enable a full 3D mapping of H*(10) inside the treatment room. Finally, the work focused on upgrading the literature model to clinically relevant configurations considering modulated beams, open collimators, patient-induced neutron fluctuations, etc. Results: The MC-based analytical model efficiently reproduced simulated H*(10) values with a maximum difference below 10%. In addition, it succeeded in predicting measured H*(10) values with differences <40%. The highest differences were registered at the closest and farthest positions from isocenter where the analytical model failed to faithfully reproduce the high neutron fluence and energy variations. The differences remains however acceptable taking into account the high measurement/simulation uncertainties and the end use of this model, i.e. radiation protection. Moreover, the model was successfully (differences < 20% on simulations and < 45% on measurements) extended to predict neutrons in the vertical direction with respect to the beam line as patients are in the upright seated position during ocular treatments. Accounting for the impact of beam modulation, collimation and the present of a patient in the beam path is far more challenging and conversion coefficients are currently being defined to predict stray neutrons in clinically representative treatment configurations. Conclusion

  10. Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction

    Energy Technology Data Exchange (ETDEWEB)

    Soardi, Gian Alberto; Perandini, Simone; Motton, Massimiliano; Montemezzi, Stefania [AOUI Verona, UOC Radiologia, Ospedale Maggiore di Borgo Trento, Verona (Italy); Larici, Anna Rita; Del Ciello, Annemilia [Universita Cattolica del Sacro Cuore, Dipartimento di Scienze Radiologiche, Roma (Italy); Rizzardi, Giovanna [UO Chirurgia Toracica, Ospedale Humanitas Gavazzeni, Bergamo (Italy); Solazzo, Antonio [UO Radiologia, Ospedale Humanitas Gavazzeni, Bergamo (Italy); Mancino, Laura [UO Pneumologia, Ospedale dell' Angelo di Mestre, Mestre (Italy); Bernhart, Marco [UO Radiologia, Ospedale dell' Angelo di Mestre, Mestre (Italy)

    2017-05-15

    To provide multicentre external validation of the Bayesian Inference Malignancy Calculator (BIMC) model by assessing diagnostic accuracy in a cohort of solitary pulmonary nodules (SPNs) collected in a clinic-based setting. To assess model impact on SPN decision analysis and to compare findings with those obtained via the Mayo Clinic model. Clinical and imaging data were retrospectively collected from 200 patients from three centres. Accuracy was assessed by means of receiver-operating characteristic (ROC) areas under the curve (AUCs). Decision analysis was performed by adopting both the American College of Chest Physicians (ACCP) and the British Thoracic Society (BTS) risk thresholds. ROC analysis showed an AUC of 0.880 (95 % CI, 0.832-0.928) for the BIMC model and of 0.604 (95 % CI, 0.524-0.683) for the Mayo Clinic model. Difference was 0.276 (95 % CI, 0.190-0.363, P < 0.0001). Decision analysis showed a slightly reduced number of false-negative and false-positive results when using ACCP risk thresholds. The BIMC model proved to be an accurate tool when characterising SPNs. In a clinical setting it can distinguish malignancies from benign nodules with minimal errors by adopting current ACCP or BTS risk thresholds and guiding lesion-tailored diagnostic and interventional procedures during the work-up. (orig.)

  11. Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

    Science.gov (United States)

    Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

    2016-08-01

    Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

  12. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study.

    Directory of Open Access Journals (Sweden)

    Kevin Ten Haaf

    2017-04-01

    Full Text Available Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years. Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer.Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST participants (1,925 lung cancer cases and 884 lung cancer deaths and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths. Six-year lung cancer incidence and mortality risk predictions were assessed for (1 calibration (graphically by comparing the agreement between the predicted and the observed risks, (2 discrimination (area under the receiver operating characteristic curve [AUC] between individuals with and without lung cancer (death, and (3 clinical usefulness (net benefit in decision curve analysis by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81. The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a

  13. External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

    Science.gov (United States)

    Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

    2016-08-30

     To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.  External validation of all published prognostic models in large scale, prospective, multicentre cohort study.  31 independent midwifery practices and six hospitals in the Netherlands.  Women recruited in their first trimester (diabetes mellitus of any type were excluded.  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  14. Validation of the close-to-delivery prediction model for vaginal birth after cesarean delivery in a Middle Eastern cohort.

    Science.gov (United States)

    Abdel Aziz, Ahmed; Abd Rabbo, Amal; Sayed Ahmed, Waleed A; Khamees, Rasha E; Atwa, Khaled A

    2016-07-01

    To validate a prediction model for vaginal birth after cesarean (VBAC) that incorporates variables available at admission for delivery among Middle Eastern women. The present prospective cohort study enrolled women at 37weeks of pregnancy or more with cephalic presentation who were willing to attempt a trial of labor (TOL) after a single prior low transverse cesarean delivery at Al-Jahra Hospital, Kuwait, between June 2013 and June 2014. The predicted success rate of VBAC determined via the close-to-delivery prediction model of Grobman et al. was compared between participants whose TOL was and was not successful. Among 203 enrolled women, 140 (69.0%) had successful VBAC. The predicted VBAC success rate was higher among women with successful TOL (82.4%±13.1%) than among those with failed TOL (67.7%±18.3%; P30%-40% to >90%-100%, the actual success rate was 20%, 30.7%, 38.5%, 59.1%, 71.4%, 76%, and 84.5%, respectively (r=0.98, P=0.013). The close-to-delivery prediction model was found to be applicable to Middle Eastern women and might predict VBAC success rates, thereby decreasing morbidities associated with failed TOL. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

  15. Development and validation of a dynamic outcome prediction model for paracetamol-induced acute liver failure

    DEFF Research Database (Denmark)

    Bernal, William; Wang, Yanzhong; Maggs, James

    2016-01-01

    : The models developed here show very good discrimination and calibration, confirmed in independent datasets, and suggest that many patients undergoing transplantation based on existing criteria might have survived with medical management alone. The role and indications for emergency liver transplantation......BACKGROUND: Early, accurate prediction of survival is central to management of patients with paracetamol-induced acute liver failure to identify those needing emergency liver transplantation. Current prognostic tools are confounded by recent improvements in outcome independent of emergency liver...... transplantation, and constrained by static binary outcome prediction. We aimed to develop a simple prognostic tool to reflect current outcomes and generate a dynamic updated estimation of risk of death. METHODS: Patients with paracetamol-induced acute liver failure managed at intensive care units in the UK...

  16. Mortality after Spontaneous Subarachnoid Hemorrhage: Causality and Validation of a Prediction Model.

    Science.gov (United States)

    Abulhasan, Yasser B; Alabdulraheem, Najayeb; Simoneau, Gabrielle; Angle, Mark R; Teitelbaum, Jeanne

    2018-04-01

    To evaluate primary causes of death after spontaneous subarachnoid hemorrhage (SAH) and externally validate the HAIR score, a prognostication tool, in a single academic institution. We reviewed all patients with SAH admitted to our neuro-intensive care unit between 2010 and 2016. Univariate and multivariate logistic regressions were performed to identify predictors of in-hospital mortality. The HAIR score predictors were Hunt and Hess grade at treatment decision, age, intraventricular hemorrhage, and rebleeding within 24 hours. Validation of the HAIR score was characterized with the receiver operating curve, the area under the curve, and a calibration plot. Among 434 patients with SAH, in-hospital mortality was 14.1%. Of the 61 mortalities, 54 (88.5%) had a neurologic cause of death or withdrawal of care and 7 (11.5%) had cardiac death. Median time from SAH to death was 6 days. The main causes of death were effect of the initial hemorrhage (26.2%), rebleeding (23%) and refractory cerebral edema (19.7%). Factors significantly associated with in-hospital mortality in the multivariate analysis were age, Hunt and Hess grade, and intracerebral hemorrhage. Maximum lumen size was also a significant risk factor after aneurysmal SAH. The HAIR score had a satisfactory discriminative ability, with an area under the curve of 0.89. The in-hospital mortality is lower than in previous reports, attesting to the continuing improvement of our institutional SAH care. The major causes are the same as in previous reports. Despite a different therapeutic protocol, the HAIR score showed good discrimination and could be a useful tool for predicting mortality. Copyright © 2018 Elsevier Inc. All rights reserved.

  17. Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

    DEFF Research Database (Denmark)

    Winkler Wille, Mathilde M.; van Riel, Sarah J.; Saghir, Zaigham

    2015-01-01

    Objectives: Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. Methods: From...... the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were...... used to evaluate risk discrimination. Results: AUCs of 0.826–0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer...

  18. Comparison of mortality prediction models and validation of SAPS II in critically ill burns patients.

    Science.gov (United States)

    Pantet, O; Faouzi, M; Brusselaers, N; Vernay, A; Berger, M M

    2016-06-30

    Specific burn outcome prediction scores such as the Abbreviated Burn Severity Index (ABSI), Ryan, Belgian Outcome of Burn Injury (BOBI) and revised Baux scores have been extensively studied. Validation studies of the critical care score SAPS II (Simplified Acute Physiology Score) have included burns patients but not addressed them as a cohort. The study aimed at comparing their performance in a Swiss burns intensive care unit (ICU) and to observe whether they were affected by a standardized definition of inhalation injury. We conducted a retrospective cohort study, including all consecutive ICU burn admissions (n=492) between 1996 and 2013: 5 epochs were defined by protocol changes. As required for SAPS II calculation, stays burned (TBSA) and inhalation injury (systematic standardized diagnosis since 2006). Study epochs were compared (χ2 test, ANOVA). Score performance was assessed by receiver operating characteristic curve analysis. SAPS II performed well (AUC 0.89), particularly in burns burns <40% TBSA. Ryan and BOBI scores were least accurate, as they heavily weight inhalation injury.

  19. [Risk Prediction Using Routine Data: Development and Validation of Multivariable Models Predicting 30- and 90-day Mortality after Surgical Treatment of Colorectal Cancer].

    Science.gov (United States)

    Crispin, Alexander; Strahwald, Brigitte; Cheney, Catherine; Mansmann, Ulrich

    2018-06-04

    Quality control, benchmarking, and pay for performance (P4P) require valid indicators and statistical models allowing adjustment for differences in risk profiles of the patient populations of the respective institutions. Using hospital remuneration data for measuring quality and modelling patient risks has been criticized by clinicians. Here we explore the potential of prediction models for 30- and 90-day mortality after colorectal cancer surgery based on routine data. Full census of a major statutory health insurer. Surgical departments throughout the Federal Republic of Germany. 4283 and 4124 insurants with major surgery for treatment of colorectal cancer during 2013 and 2014, respectively. Age, sex, primary and secondary diagnoses as well as tumor locations as recorded in the hospital remuneration data according to §301 SGB V. 30- and 90-day mortality. Elixhauser comorbidities, Charlson conditions, and Charlson scores were generated from the ICD-10 diagnoses. Multivariable prediction models were developed using a penalized logistic regression approach (logistic ridge regression) in a derivation set (patients treated in 2013). Calibration and discrimination of the models were assessed in an internal validation sample (patients treated in 2014) using calibration curves, Brier scores, receiver operating characteristic curves (ROC curves) and the areas under the ROC curves (AUC). 30- and 90-day mortality rates in the learning-sample were 5.7 and 8.4%, respectively. The corresponding values in the validation sample were 5.9% and once more 8.4%. Models based on Elixhauser comorbidities exhibited the highest discriminatory power with AUC values of 0.804 (95% CI: 0.776 -0.832) and 0.805 (95% CI: 0.782-0.828) for 30- and 90-day mortality. The Brier scores for these models were 0.050 (95% CI: 0.044-0.056) and 0.067 (95% CI: 0.060-0.074) and similar to the models based on Charlson conditions. Regardless of the model, low predicted probabilities were well calibrated, while

  20. Validation and Application of Models to Predict Facemask Influenza Contamination in Healthcare Settings

    Science.gov (United States)

    Fisher, Edward M.; Noti, John D.; Lindsley, William G.; Blachere, Francoise M.; Shaffer, Ronald E.

    2015-01-01

    Facemasks are part of the hierarchy of interventions used to reduce the transmission of respiratory pathogens by providing a barrier. Two types of facemasks used by healthcare workers are N95 filtering facepiece respirators (FFRs) and surgical masks (SMs). These can become contaminated with respiratory pathogens during use, thus serving as potential sources for transmission. However, because of the lack of field studies, the hazard associated with pathogen-exposed facemasks is unknown. A mathematical model was used to calculate the potential influenza contamination of facemasks from aerosol sources in various exposure scenarios. The aerosol model was validated with data from previous laboratory studies using facemasks mounted on headforms in a simulated healthcare room. The model was then used to estimate facemask contamination levels in three scenarios generated with input parameters from the literature. A second model estimated facemask contamination from a cough. It was determined that contamination levels from a single cough (≈19 viruses) were much less than likely levels from aerosols (4,473 viruses on FFRs and 3,476 viruses on SMs). For aerosol contamination, a range of input values from the literature resulted in wide variation in estimated facemask contamination levels (13–202,549 viruses), depending on the values selected. Overall, these models and estimates for facemask contamination levels can be used to inform infection control practice and research related to the development of better facemasks, to characterize airborne contamination levels, and to assist in assessment of risk from reaerosolization and fomite transfer because of handling and reuse of contaminated facemasks. PMID:24593662

  1. Building and validation of a prognostic model for predicting extracorporeal circuit clotting in patients with continuous renal replacement therapy.

    Science.gov (United States)

    Fu, Xia; Liang, Xinling; Song, Li; Huang, Huigen; Wang, Jing; Chen, Yuanhan; Zhang, Li; Quan, Zilin; Shi, Wei

    2014-04-01

    To develop a predictive model for circuit clotting in patients with continuous renal replacement therapy (CRRT). A total of 425 cases were selected. 302 cases were used to develop a predictive model of extracorporeal circuit life span during CRRT without citrate anticoagulation in 24 h, and 123 cases were used to validate the model. The prediction formula was developed using multivariate Cox proportional-hazards regression analysis, from which a risk score was assigned. The mean survival time of the circuit was 15.0 ± 1.3 h, and the rate of circuit clotting was 66.6 % during 24 h of CRRT. Five significant variables were assigned a predicting score according to the regression coefficient: insufficient blood flow, no anticoagulation, hematocrit ≥0.37, lactic acid of arterial blood gas analysis ≤3 mmol/L and APTT R (2) = 0.232; P = 0.301). A risk score that includes the five above-mentioned variables can be used to predict the likelihood of extracorporeal circuit clotting in patients undergoing CRRT.

  2. Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

    Science.gov (United States)

    Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

    2018-05-01

    The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential outcomes after coronary artery bypass grafting. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  3. Predictive validity of a three-dimensional model of performance anxiety in the context of tae-kwon-do.

    Science.gov (United States)

    Cheng, Wen-Nuan Kara; Hardy, Lew; Woodman, Tim

    2011-02-01

    We tested the predictive validity of the recently validated three-dimensional model of performance anxiety (Chang, Hardy, & Markland, 2009) with elite tae-kwon-do competitors (N = 99). This conceptual framework emphasized the adaptive potential of anxiety by including a regulatory dimension (reflected by perceived control) along with the intensity-oriented dimensions of cognitive and physiological anxiety. Anxiety was assessed 30 min before a competitive contest using the Three-Factor Anxiety Inventory. Competitors rated their performance on a tae-kwon-do-specific performance scale within 30 min after completion of their contest. Moderated hierarchical regression analyses revealed initial support for the predictive validity of the three-dimensional performance anxiety model. The regulatory dimension of anxiety (perceived control) revealed significant main and interactive effects on performance. This dimension appeared to be adaptive, as performance was better under high than low perceived control, and best vs. worst performance was associated with highest vs. lowest perceived control, respectively. Results are discussed in terms of the importance of the regulatory dimension of anxiety.

  4. Evaluation of the phototoxicity of unsubstituted and alkylated polycyclic aromatic hydrocarbons to mysid shrimp (Americamysis bahia): Validation of predictive models.

    Science.gov (United States)

    Finch, Bryson E; Marzooghi, Solmaz; Di Toro, Dominic M; Stubblefield, William A

    2017-08-01

    Crude oils are composed of an assortment of hydrocarbons, some of which are polycyclic aromatic hydrocarbons (PAHs). Polycyclic aromatic hydrocarbons are of particular interest due to their narcotic and potential phototoxic effects. Several studies have examined the phototoxicity of individual PAHs and fresh and weathered crude oils, and several models have been developed to predict PAH toxicity. Fingerprint analyses of oils have shown that PAHs in crude oils are predominantly alkylated. However, current models for estimating PAH phototoxicity assume toxic equivalence between unsubstituted (i.e., parent) and alkyl-substituted compounds. This approach may be incorrect if substantial differences in toxic potency exist between unsubstituted and substituted PAHs. The objective of the present study was to examine the narcotic and photo-enhanced toxicity of commercially available unsubstituted and alkylated PAHs to mysid shrimp (Americamysis bahia). Data were used to validate predictive models of phototoxicity based on the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap approach and to develop relative effect potencies. Results demonstrated that photo-enhanced toxicity increased with increasing methylation and that phototoxic PAH potencies vary significantly among unsubstituted compounds. Overall, predictive models based on the HOMO-LUMO gap were relatively accurate in predicting phototoxicity for unsubstituted PAHs but are limited to qualitative assessments. Environ Toxicol Chem 2017;36:2043-2049. © 2017 SETAC. © 2017 SETAC.

  5. Validation of the kinetic model for predicting the composition of chlorinated water discharged from power plant cooling systems

    International Nuclear Information System (INIS)

    Lietzke, M.H.

    1977-01-01

    The purpose of this report is to present a validation of a previously described kinetic model which was developed to predict the composition of chlorinated fresh water discharged from power plant cooling systems. The model was programmed in two versions: as a stand-alone program and as a part of a unified transport model developed from consistent mathematical models to simulate the dispersion of heated water and radioisotopic and chemical effluents from power plant discharges. The results of testing the model using analytical data taken during operation of the once-through cooling system of the Quad Cities Nuclear Station are described. Calculations are also presented on the Three Mile Island Nuclear Station which uses cooling towers

  6. Development and validation of a predictive risk model for all-cause mortality in type 2 diabetes.

    Science.gov (United States)

    Robinson, Tom E; Elley, C Raina; Kenealy, Tim; Drury, Paul L

    2015-06-01

    Type 2 diabetes is common and is associated with an approximate 80% increase in the rate of mortality. Management decisions may be assisted by an estimate of the patient's absolute risk of adverse outcomes, including death. This study aimed to derive a predictive risk model for all-cause mortality in type 2 diabetes. We used primary care data from a large national multi-ethnic cohort of patients with type 2 diabetes in New Zealand and linked mortality records to develop a predictive risk model for 5-year risk of mortality. We then validated this model using information from a separate cohort of patients with type 2 diabetes. 26,864 people were included in the development cohort with a median follow up time of 9.1 years. We developed three models initially using demographic information and then progressively more clinical detail. The final model, which also included markers of renal disease, proved to give best prediction of all-cause mortality with a C-statistic of 0.80 in the development cohort and 0.79 in the validation cohort (7610 people) and was well calibrated. Ethnicity was a major factor with hazard ratios of 1.37 for indigenous Maori, 0.41 for East Asian and 0.55 for Indo Asian compared with European (P<0.001). We have developed a model using information usually available in primary care that provides good assessment of patient's risk of death. Results are similar to models previously published from smaller cohorts in other countries and apply to a wider range of patient ethnic groups. Copyright © 2015. Published by Elsevier Ireland Ltd.

  7. Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases.

    Science.gov (United States)

    Hwang, Eui Jin; Goo, Jin Mo; Kim, Jihye; Park, Sang Joon; Ahn, Soyeon; Park, Chang Min; Shin, Yeong-Gil

    2017-08-01

    To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth. For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE). SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold. • The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.

  8. Predictive accuracy of the PanCan lung cancer risk prediction model - external validation based on CT from the Danish Lung Cancer Screening Trial

    International Nuclear Information System (INIS)

    Winkler Wille, Mathilde M.; Dirksen, Asger; Riel, Sarah J. van; Jacobs, Colin; Scholten, Ernst T.; Ginneken, Bram van; Saghir, Zaigham; Pedersen, Jesper Holst; Hohwue Thomsen, Laura; Skovgaard, Lene T.

    2015-01-01

    Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination. AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

  9. Predictive accuracy of the PanCan lung cancer risk prediction model - external validation based on CT from the Danish Lung Cancer Screening Trial

    Energy Technology Data Exchange (ETDEWEB)

    Winkler Wille, Mathilde M.; Dirksen, Asger [Gentofte Hospital, Department of Respiratory Medicine, Hellerup (Denmark); Riel, Sarah J. van; Jacobs, Colin; Scholten, Ernst T.; Ginneken, Bram van [Radboud University Medical Center, Department of Radiology and Nuclear Medicine, Nijmegen (Netherlands); Saghir, Zaigham [Herlev Hospital, Department of Respiratory Medicine, Herlev (Denmark); Pedersen, Jesper Holst [Copenhagen University Hospital, Department of Thoracic Surgery, Rigshospitalet, Koebenhavn Oe (Denmark); Hohwue Thomsen, Laura [Hvidovre Hospital, Department of Respiratory Medicine, Hvidovre (Denmark); Skovgaard, Lene T. [University of Copenhagen, Department of Biostatistics, Koebenhavn Oe (Denmark)

    2015-10-15

    Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination. AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. (orig.)

  10. External validation of the Intensive Care National Audit & Research Centre (ICNARC) risk prediction model in critical care units in Scotland.

    Science.gov (United States)

    Harrison, David A; Lone, Nazir I; Haddow, Catriona; MacGillivray, Moranne; Khan, Angela; Cook, Brian; Rowan, Kathryn M

    2014-01-01

    Risk prediction models are used in critical care for risk stratification, summarising and communicating risk, supporting clinical decision-making and benchmarking performance. However, they require validation before they can be used with confidence, ideally using independently collected data from a different source to that used to develop the model. The aim of this study was to validate the Intensive Care National Audit & Research Centre (ICNARC) model using independently collected data from critical care units in Scotland. Data were extracted from the Scottish Intensive Care Society Audit Group (SICSAG) database for the years 2007 to 2009. Recoding and mapping of variables was performed, as required, to apply the ICNARC model (2009 recalibration) to the SICSAG data using standard computer algorithms. The performance of the ICNARC model was assessed for discrimination, calibration and overall fit and compared with that of the Acute Physiology And Chronic Health Evaluation (APACHE) II model. There were 29,626 admissions to 24 adult, general critical care units in Scotland between 1 January 2007 and 31 December 2009. After exclusions, 23,269 admissions were included in the analysis. The ICNARC model outperformed APACHE II on measures of discrimination (c index 0.848 versus 0.806), calibration (Hosmer-Lemeshow chi-squared statistic 18.8 versus 214) and overall fit (Brier's score 0.140 versus 0.157; Shapiro's R 0.652 versus 0.621). Model performance was consistent across the three years studied. The ICNARC model performed well when validated in an external population to that in which it was developed, using independently collected data.

  11. Validation of a predictive model for the growth of chalk yeasts on bread.

    Science.gov (United States)

    Burgain, Anaïs; Bensoussan, Maurice; Dantigny, Philippe

    2015-07-02

    The present study focused on the effects of temperature, T, and water activity, aw, on the growth of Hyphopichia burtonii, Pichia anomala, and Saccharomycopsis fibuligera on Sabouraud Agar Medium. Cardinal values were estimated by means of cardinal models with inflection. All the yeasts were xerophilic, and they exhibited growth at 0.85 aw. The combined effects of T, aw, and pH on the growth of these species were described by the gamma-concept and validated on bread in the range of 15-25 °C, 0.91-0.97 aw, and pH 4.6-6.8. The optimum growth rates on bread were 2.88, 0.259, and 1.06 mm/day for H. burtonii, P. anomala, and S. fibuligera, respectively. The optimal growth rate of S. fibuligera on bread was about 2 fold that obtained on Sabouraud. Due to reproduction by budding, P. anomala exhibited low growth on Sabouraud and bread. However, this species is of major concern in the baker's industry because of the production of ethyl acetate in bread. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Predictors of outcome after elective endovascular abdominal aortic aneurysm repair and external validation of a risk prediction model.

    Science.gov (United States)

    Wisniowski, Brendan; Barnes, Mary; Jenkins, Jason; Boyne, Nicholas; Kruger, Allan; Walker, Philip J

    2011-09-01

    Endovascular abdominal aortic aneurysm (AAA) repair (EVAR) has been associated with lower operative mortality and morbidity than open surgery but comparable long-term mortality and higher delayed complication and reintervention rates. Attention has therefore been directed to identifying preoperative and operative variables that influence outcomes after EVAR. Risk-prediction models, such as the EVAR Risk Assessment (ERA) model, have also been developed to help surgeons plan EVAR procedures. The aims of this study were (1) to describe outcomes of elective EVAR at the Royal Brisbane and Women's Hospital (RBWH), (2) to identify preoperative and operative variables predictive of outcomes after EVAR, and (3) to externally validate the ERA model. All elective EVAR procedures at the RBWH before July 1, 2009, were reviewed. Descriptive analyses were performed to determine the outcomes. Univariate and multivariate analyses were performed to identify preoperative and operative variables predictive of outcomes after EVAR. Binomial logistic regression analyses were used to externally validate the ERA model. Before July 1, 2009, 197 patients (172 men), who were a mean age of 72.8 years, underwent elective EVAR at the RBWH. Operative mortality was 1.0%. Survival was 81.1% at 3 years and 63.2% at 5 years. Multivariate analysis showed predictors of survival were age (P = .0126), American Society of Anesthesiologists (ASA) score (P = .0180), and chronic obstructive pulmonary disease (P = .0348) at 3 years and age (P = .0103), ASA score (P = .0006), renal failure (P = .0048), and serum creatinine (P = .0022) at 5 years. Aortic branch vessel score was predictive of initial (30-day) type II endoleak (P = .0015). AAA tortuosity was predictive of midterm type I endoleak (P = .0251). Female sex was associated with lower rates of initial clinical success (P = .0406). The ERA model fitted RBWH data well for early death (C statistic = .906), 3-year survival (C statistic = .735), 5-year

  13. Model Verification and Validation Concepts for a Probabilistic Fracture Assessment Model to Predict Cracking of Knife Edge Seals in the Space Shuttle Main Engine High Pressure Oxidizer

    Science.gov (United States)

    Pai, Shantaram S.; Riha, David S.

    2013-01-01

    Physics-based models are routinely used to predict the performance of engineered systems to make decisions such as when to retire system components, how to extend the life of an aging system, or if a new design will be safe or available. Model verification and validation (V&V) is a process to establish credibility in model predictions. Ideally, carefully controlled validation experiments will be designed and performed to validate models or submodels. In reality, time and cost constraints limit experiments and even model development. This paper describes elements of model V&V during the development and application of a probabilistic fracture assessment model to predict cracking in space shuttle main engine high-pressure oxidizer turbopump knife-edge seals. The objective of this effort was to assess the probability of initiating and growing a crack to a specified failure length in specific flight units for different usage and inspection scenarios. The probabilistic fracture assessment model developed in this investigation combined a series of submodels describing the usage, temperature history, flutter tendencies, tooth stresses and numbers of cycles, fatigue cracking, nondestructive inspection, and finally the probability of failure. The analysis accounted for unit-to-unit variations in temperature, flutter limit state, flutter stress magnitude, and fatigue life properties. The investigation focused on the calculation of relative risk rather than absolute risk between the usage scenarios. Verification predictions were first performed for three units with known usage and cracking histories to establish credibility in the model predictions. Then, numerous predictions were performed for an assortment of operating units that had flown recently or that were projected for future flights. Calculations were performed using two NASA-developed software tools: NESSUS(Registered Trademark) for the probabilistic analysis, and NASGRO(Registered Trademark) for the fracture

  14. Development and Validation of Computational Fluid Dynamics Models for Prediction of Heat Transfer and Thermal Microenvironments of Corals

    Science.gov (United States)

    Ong, Robert H.; King, Andrew J. C.; Mullins, Benjamin J.; Cooper, Timothy F.; Caley, M. Julian

    2012-01-01

    We present Computational Fluid Dynamics (CFD) models of the coupled dynamics of water flow, heat transfer and irradiance in and around corals to predict temperatures experienced by corals. These models were validated against controlled laboratory experiments, under constant and transient irradiance, for hemispherical and branching corals. Our CFD models agree very well with experimental studies. A linear relationship between irradiance and coral surface warming was evident in both the simulation and experimental result agreeing with heat transfer theory. However, CFD models for the steady state simulation produced a better fit to the linear relationship than the experimental data, likely due to experimental error in the empirical measurements. The consistency of our modelling results with experimental observations demonstrates the applicability of CFD simulations, such as the models developed here, to coral bleaching studies. A study of the influence of coral skeletal porosity and skeletal bulk density on surface warming was also undertaken, demonstrating boundary layer behaviour, and interstitial flow magnitude and temperature profiles in coral cross sections. Our models compliment recent studies showing systematic changes in these parameters in some coral colonies and have utility in the prediction of coral bleaching. PMID:22701582

  15. External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

    NARCIS (Netherlands)

    Lamain-de Ruiter, M.; Kwee, A.; Naaktgeboren, C.A.; Groot, I. de; Evers, I.M.; Groenendaal, F.; Hering, Y.R.; Huisjes, A.J.M.; Kirpestein, C.; Monincx, W.M.; Siljee, J.E.; Zelfde, A. van't; Oirschot, C.M. van; Vankan-Buitelaar, S.A.; Vonk, M.A.A.W.; Wiegers, T.A.; Zwart, J.J.; Franx, A.; Moons, K.G.M.; Koster, M.P.H.

    2016-01-01

    Objective: To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. Design: External validation of all published prognostic models in

  16. A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

    Energy Technology Data Exchange (ETDEWEB)

    Oberije, Cary, E-mail: cary.oberije@maastro.nl [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); De Ruysscher, Dirk [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Universitaire Ziekenhuizen Leuven, KU Leuven (Belgium); Houben, Ruud [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands); Heuvel, Michel van de; Uyterlinde, Wilma [Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Deasy, Joseph O. [Memorial Sloan Kettering Cancer Center, New York (United States); Belderbos, Jose [Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands); Dingemans, Anne-Marie C. [Department of Pulmonology, University Hospital Maastricht, Research Institute GROW of Oncology, Maastricht (Netherlands); Rimner, Andreas; Din, Shaun [Memorial Sloan Kettering Cancer Center, New York (United States); Lambin, Philippe [Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands)

    2015-07-15

    Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

  17. Validation of prediction model for successful vaginal birth after Cesarean delivery based on sonographic assessment of hysterotomy scar.

    Science.gov (United States)

    Baranov, A; Salvesen, K Å; Vikhareva, O

    2018-02-01

    To validate a prediction model for successful vaginal birth after Cesarean delivery (VBAC) based on sonographic assessment of the hysterotomy scar, in a Swedish population. Data were collected from a prospective cohort study. We recruited non-pregnant women aged 18-35 years who had undergone one previous low-transverse Cesarean delivery at ≥ 37 gestational weeks and had had no other uterine surgery. Participants who subsequently became pregnant underwent transvaginal ultrasound examination of the Cesarean hysterotomy scar at 11 + 0 to 13 + 6 and at 19 + 0 to 21 + 6 gestational weeks. Thickness of the myometrium at the thinnest part of the scar area was measured. After delivery, information on pregnancy outcome was retrieved from hospital records. Individual probabilities of successful VBAC were calculated using a previously published model. Predicted individual probabilities were divided into deciles. For each decile, observed VBAC rates were calculated. To assess the accuracy of the prediction model, receiver-operating characteristics curves were constructed and the areas under the curves (AUC) were calculated. Complete sonographic data were available for 120 women. Eighty (67%) women underwent trial of labor after Cesarean delivery (TOLAC) with VBAC occurring in 70 (88%) cases. The scar was visible in all 80 women at the first-trimester scan and in 54 (68%) women at the second-trimester scan. AUC was 0.44 (95% CI, 0.28-0.60) among all women who underwent TOLAC and 0.51 (95% CI, 0.32-0.71) among those with the scar visible sonographically at both ultrasound examinations. The prediction model demonstrated poor accuracy for prediction of successful VBAC in our Swedish population. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

  18. The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing.

    Directory of Open Access Journals (Sweden)

    Jaclyn K Mann

    2014-08-01

    Full Text Available Viral immune evasion by sequence variation is a major hindrance to HIV-1 vaccine design. To address this challenge, our group has developed a computational model, rooted in physics, that aims to predict the fitness landscape of HIV-1 proteins in order to design vaccine immunogens that lead to impaired viral fitness, thus blocking viable escape routes. Here, we advance the computational models to address previous limitations, and directly test model predictions against in vitro fitness measurements of HIV-1 strains containing multiple Gag mutations. We incorporated regularization into the model fitting procedure to address finite sampling. Further, we developed a model that accounts for the specific identity of mutant amino acids (Potts model, generalizing our previous approach (Ising model that is unable to distinguish between different mutant amino acids. Gag mutation combinations (17 pairs, 1 triple and 25 single mutations within these predicted to be either harmful to HIV-1 viability or fitness-neutral were introduced into HIV-1 NL4-3 by site-directed mutagenesis and replication capacities of these mutants were assayed in vitro. The predicted and measured fitness of the corresponding mutants for the original Ising model (r = -0.74, p = 3.6×10-6 are strongly correlated, and this was further strengthened in the regularized Ising model (r = -0.83, p = 3.7×10-12. Performance of the Potts model (r = -0.73, p = 9.7×10-9 was similar to that of the Ising model, indicating that the binary approximation is sufficient for capturing fitness effects of common mutants at sites of low amino acid diversity. However, we show that the Potts model is expected to improve predictive power for more variable proteins. Overall, our results support the ability of the computational models to robustly predict the relative fitness of mutant viral strains, and indicate the potential value of this approach for understanding viral immune evasion

  19. External validation of prediction models for time to death in potential donors after circulatory death

    NARCIS (Netherlands)

    Kotsopoulos, A.M.M.; Böing-Messing, F.; Jansen, N.E.; Vos, P.; Abdo, W.F.

    2018-01-01

    Predicting time to death in controlled donation after circulatory death (cDCD) donors following withdrawal of life‐sustaining treatment (WLST) is important but poses a major challenge. The aim of this study is to determine factors predicting time to circulatory death within 60 minutes after WSLT and

  20. Predicting the peak growth velocity in the individual child: validation of a new growth model.

    NARCIS (Netherlands)

    Busscher, I.; Kingma, I.; de Bruin, R.; Wapstra, F.H.; Verkerke, G.J.; Veldhuizen, A.G.

    2012-01-01

    Predicting the peak growth velocity in an individual patient with adolescent idiopathic scoliosis is essential or determining the prognosis of the disorder and timing of the (surgical) treatment. Until the present time, no accurate method has been found to predict the timing and magnitude of the

  1. Predicting the peak growth velocity in the individual child : validation of a new growth model

    NARCIS (Netherlands)

    Busscher, Iris; Kingma, Idsart; de Bruin, Rob; Wapstra, Frits Hein; Verkerke, Gijsvertus J.; Veldhuizen, Albert G.

    Predicting the peak growth velocity in an individual patient with adolescent idiopathic scoliosis is essential or determining the prognosis of the disorder and timing of the (surgical) treatment. Until the present time, no accurate method has been found to predict the timing and magnitude of the

  2. Predicting the peak growth velocity in the individual child: validation of a new growth model

    NARCIS (Netherlands)

    Busscher, I.; Kingma, I.; Bruin, R.; Wapstra, F.H.; Verkerke, Gijsbertus Jacob; Veldhuizen, A.G.

    2012-01-01

    Predicting the peak growth velocity in an individual patient with adolescent idiopathic scoliosis is essential or determining the prognosis of the disorder and timing of the (surgical) treatment. Until the present time, no accurate method has been found to predict the timing and magnitude of the

  3. Validity of the CR-POSSUM model in surgery for colorectal cancer in Spain (CCR-CARESS study) and comparison with other models to predict operative mortality.

    Science.gov (United States)

    Baré, Marisa; Alcantara, Manuel Jesús; Gil, Maria José; Collera, Pablo; Pont, Marina; Escobar, Antonio; Sarasqueta, Cristina; Redondo, Maximino; Briones, Eduardo; Dujovne, Paula; Quintana, Jose Maria

    2018-01-29

    To validate and recalibrate the CR- POSSUM model and compared its discriminatory capacity with other European models such as POSSUM, P-POSSUM, AFC or IRCS to predict operative mortality in surgery for colorectal cancer. Prospective multicenter cohort study from 22 hospitals in Spain. We included patients undergoing planned or urgent surgery for primary invasive colorectal cancers between June 2010 and December 2012 (N = 2749). Clinical data were gathered through medical chart review. We validated and recalibrated the predictive models using logistic regression techniques. To calculate the discriminatory power of each model, we estimated the areas under the curve - AUC (95% CI). We also assessed the calibration of the models by applying the Hosmer-Lemeshow test. In-hospital mortality was 1.5% and 30-day mortality, 1.7%. In the validation process, the discriminatory power of the CR-POSSUM for predicting in-hospital mortality was 73.6%. However, in the recalibration process, the AUCs improved slightly: the CR-POSSUM reached 75.5% (95% CI: 67.3-83.7). The discriminatory power of the CR-POSSUM for predicting 30-day mortality was 74.2% (95% CI: 67.1-81.2) after recalibration; among the other models the POSSUM had the greatest discriminatory power, with an AUC of 77.0% (95% CI: 68.9-85.2). The Hosmer-Lemeshow test showed good fit for all the recalibrated models. The CR-POSSUM and the other models showed moderate capacity to discriminate the risk of operative mortality in our context, where the actual operative mortality is low. Nevertheless the IRCS might better predict in-hospital mortality, with fewer variables, while the CR-POSSUM could be slightly better for predicting 30-day mortality. Registered at: ClinicalTrials.gov Identifier: NCT02488161.

  4. External validation of two prediction models identifying employees at risk of high sickness absence : cohort study with 1-year follow-up

    NARCIS (Netherlands)

    Roelen, Corne A. M.; Bultmann, Ute; van Rhenen, Willem; van der Klink, Jac J. L.; Twisk, Jos W. R.; Heymans, Martijn W.

    2013-01-01

    Background: Two models including age, self-rated health (SRH) and prior sickness absence (SA) were found to predict high SA in health care workers. The present study externally validated these prediction models in a population of office workers and investigated the effect of adding gender as a

  5. Developing and validating of predictive model for radiofrequency radiation emission within the vicinity of fm stations in Ghana

    International Nuclear Information System (INIS)

    Ahenkora-Duodu, Kingsley

    2016-07-01

    The rapid growing number of FM stations with their corresponding antennas have led to an increase in the concern of the potential health risks that may arise as a result of exposure to RF radiations. The main objective of this research was to develop and validate a predictive model with real time measured data for FM antennas in Ghana. Theoretical and experimental assessment of radiofrequency emission due to FM antennas has been analysed. The maximum and minimum electric field spatial average recorded was 7.17E-01 ± 6.97E-01V/m at Kasapa FM and 6.39E-02 ± 5.39E-02V/m at Asempa FM respectively. At a transmission frequency range of 88 -108 MHz, the average power density of the real time measured data ranged between 3.92E-05W/m"2 and 1.37E-03W/m"2 whiles that of the FM model varied from 9.72E-03W/m"2 to 5.35E-01W/m"2 respectively. Results obtained showed a variation between measured power density levels and the FM model. The FM model overestimates the power density levels as compared to that of the measured data. The impact predictions were based on the maximum values estimated by the FM model, hence these results validates the credibility of the impact analysis for the FM stations. The general public exposure quotient ranged between 9.00E-03 and 2.68E-01 whilst that of the occupational exposure quotient varied from 9.72E-04 to 5.35E-02. The results obtained were found to be in compliance with the International Commission on Non-Ionizing Radiation Protection (ICNIRP) RF exposure limit. (au)

  6. Three-dimensional fuel pin model validation by prediction of hydrogen distribution in cladding and comparison with experiment

    Energy Technology Data Exchange (ETDEWEB)

    Aly, A. [North Carolina State Univ., Raleigh, NC (United States); Avramova, Maria [North Carolina State Univ., Raleigh, NC (United States); Ivanov, Kostadin [Pennsylvania State Univ., University Park, PA (United States); Motta, Arthur [Pennsylvania State Univ., University Park, PA (United States); Lacroix, E. [Pennsylvania State Univ., University Park, PA (United States); Manera, Annalisa [Univ. of Michigan, Ann Arbor, MI (United States); Walter, D. [Univ. of Michigan, Ann Arbor, MI (United States); Williamson, R. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Gamble, K. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2017-10-29

    To correctly describe and predict this hydrogen distribution there is a need for multi-physics coupling to provide accurate three-dimensional azimuthal, radial, and axial temperature distributions in the cladding. Coupled high-fidelity reactor-physics codes with a sub-channel code as well as with a computational fluid dynamics (CFD) tool have been used to calculate detailed temperature distributions. These high-fidelity coupled neutronics/thermal-hydraulics code systems are coupled further with the fuel-performance BISON code with a kernel (module) for hydrogen. Both hydrogen migration and precipitation/dissolution are included in the model. Results from this multi-physics analysis is validated utilizing calculations of hydrogen distribution using models informed by data from hydrogen experiments and PIE data.

  7. Derivation and validation of a multivariable model to predict when primary care physicians prescribe antidepressants for indications other than depression

    Directory of Open Access Journals (Sweden)

    Wong J

    2018-04-01

    Full Text Available Jenna Wong, Michal Abrahamowicz, David L Buckeridge, Robyn Tamblyn Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada Objective: Physicians commonly prescribe antidepressants for indications other than depression that are not evidence-based and need further evaluation. However, lack of routinely documented treatment indications for medications in administrative and medical databases creates a major barrier to evaluating antidepressant use for indications besides depression. Thus, the aim of this study was to derive a model to predict when primary care physicians prescribe antidepressants for indications other than depression and to identify important determinants of this prescribing practice. Methods: Prediction study using antidepressant prescriptions from January 2003–December 2012 in an indication-based electronic prescribing system in Quebec, Canada. Patients were linked to demographic files, medical billings data, and hospital discharge summary data to create over 370 candidate predictors. The final prediction model was derived on a random 75% sample of the data using 3-fold cross-validation integrated within a score-based forward stepwise selection procedure. The performance of the final model was assessed in the remaining 25% of the data. Results: Among 73,576 antidepressant prescriptions, 32,405 (44.0% were written for indications other than depression. Among 40 predictors in the final model, the most important covariates included the molecule name, the patient’s education level, the physician’s workload, the prescribed dose, and diagnostic codes for plausible indications recorded in the past year. The final model had good discrimination (concordance (c statistic 0.815; 95% CI, 0.787–0.847 and good calibration (ratio of observed to expected events 0.986; 95% CI, 0.842–1.136. Conclusion: In the absence of documented treatment indications, researchers may be able to use

  8. New Guideline for the Reporting of Studies Developing, Validating, or Updating a Multivariable Clinical Prediction Model : The TRIPOD Statement

    NARCIS (Netherlands)

    Moons, Karel G. M.; Altman, Douglas G.; Reitsma, Johannes B.; Collins, Gary S.

    Prediction models are developed to aid health care providers in estimating the probability that a specific outcome or disease is present (diagnostic prediction models) or will occur in the future (prognostic prediction models), to inform their decision making. Prognostic models here also include

  9. Development and validation of a predictive model for the growth of Vibrio parahaemolyticus in post-harvest shellstock oysters.

    Science.gov (United States)

    Parveen, Salina; DaSilva, Ligia; DePaola, Angelo; Bowers, John; White, Chanelle; Munasinghe, Kumudini Apsara; Brohawn, Kathy; Mudoh, Meshack; Tamplin, Mark

    2013-01-15

    Information is limited about the growth and survival of naturally-occurring Vibrio parahaemolyticus in live oysters under commercially relevant storage conditions harvested from different regions and in different oyster species. This study produced a predictive model for the growth of naturally-occurring V. parahaemolyticus in live Eastern oysters (Crassostrea virginica) harvested from the Chesapeake Bay, MD, USA and stored at 5-30 °C until oysters gapped. The model was validated with model-independent data collected from Eastern oysters harvested from the Chesapeake Bay and Mobile Bay, AL, USA and Asian (C. ariakensis) oysters from the Chesapeake Bay, VA, USA. The effect of harvest season, region and water condition on growth rate (GR) was also tested. At each time interval, two samples consisting of six oysters each were analyzed by a direct-plating method for total V. parahaemolyticus. The Baranyi D-model was fitted to the total V. parahaemolyticus growth and survival data. A secondary model was produced using the square root model. V. parahaemolyticus slowly inactivated at 5 and 10 °C with average rates of -0.002 and -0.001 log cfu/h, respectively. The average GRs at 15, 20, 25, and 30 °C were 0.038, 0.082, 0.228, and 0.219 log cfu/h, respectively. The bias and accuracy factors of the secondary model for model-independent data were 1.36 and 1.46 for Eastern oysters from Mobile Bay and the Chesapeake Bay, respectively. V. parahaemolyticus GRs were markedly lower in Asian oysters. Harvest temperature, salinity, region and season had no effect on GRs. The observed GRs were less than those predicted by the U.S. Food and Drug Administration's V. parahaemolyticus quantitative risk assessment. Copyright © 2012 Elsevier B.V. All rights reserved.

  10. On-line validation of prediction model in case of nuclear fallout

    International Nuclear Information System (INIS)

    Mueck, K.; Gerzabek, M.H.; Suda, M.; Henrich, E.

    1996-01-01

    After a large-scale nuclear fallout the early prediction of the exposure of the population to be expected is of great importance to enable early decisions on countermeasures to be taken and to optimise such decisions to ensure an maximum reduction of the expected exposure if required. This includes the prediction of the contribution of each exposure pathway, in particular the ingestion path, and the contribution of important foodstuffs to this path. The prediction should be fairly precise which requires a number of data at an early stage after the accident on a nation-wide scale. This is impeded by a number of limitations in availability of data in the early phase which are caused by various reasons: Some important data such as the activity concentration integral in air, total wet and dry deposition and radionuclide concentrations in important fodder are not available before the end of the passage of the plume and the resulting fallout. But also the time delay in taking samples of relevant items on a nation-wide scale, delivery of the samples to the measuring laboratories and the need of sample preparation especially with regard to certain non-gamma-emitting radionuclides contribute to this unavailability of data relevant for the prediction in the early phase. (author)

  11. Identification of patients at high risk for Clostridium difficile infection : Development and validation of a risk prediction model in hospitalized patients treated with antibiotics

    NARCIS (Netherlands)

    van Werkhoven, C. H.; van der Tempel, J.; Jajou, R.; Thijsen, S. F T; Diepersloot, R. J A; Bonten, M. J M; Postma, D. F.; Oosterheert, J. J.

    2015-01-01

    To develop and validate a prediction model for Clostridium difficile infection (CDI) in hospitalized patients treated with systemic antibiotics, we performed a case-cohort study in a tertiary (derivation) and secondary care hospital (validation). Cases had a positive Clostridium test and were

  12. Validating the MFiX-DEM Model for Flow Regime Prediction in a 3D Spouted Bed

    Energy Technology Data Exchange (ETDEWEB)

    Banerjee, Subhodeep [National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). Research and Innovation Center; Oak Ridge Inst. for Science and Education (ORISE), Oak Ridge, TN (United States); Guenther, Chris [National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). Research and Innovation Center; Rogers, William A. [National Energy Technology Lab. (NETL), Pittsburgh, PA, and Morgantown, WV (United States). Research and Innovation Center

    2018-02-08

    The spout-fluidized bed reactor with relatively large oxygen carrier particles offers several advantages in chemical looping combustion operation using solid fuels. The large difference in size and weight between the oxygen carrier particles and the smaller coal or ash particles allows the oxygen carrier to be easily segregated for recirculation; the increased solids mixing due to dynamic flow pattern in the spout-fluidization regime prevents agglomeration. The primary objective in this work is to determine the effectiveness of the MFiX-DEM model in predicting the flow regime in a spouted bed. Successful validation of the code will allow the user to fine tune the operating conditions of a spouted bed to achieve the desired operating condition.

  13. Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model

    Science.gov (United States)

    Tice, Jeffrey A.; Cummings, Steven R.; Smith-Bindman, Rebecca; Ichikawa, Laura; Barlow, William E.; Kerlikowske, Karla

    2009-01-01

    Background Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography. Objective To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density. Design Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort. Setting Screening mammography sites participating in the Breast Cancer Surveillance Consortium. Patients 1 095 484 women undergoing mammography who had no previous diagnosis of breast cancer. Measurements Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories. Results During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14 766 women. The breast density model was well calibrated overall (expected–observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years. Limitation The model has only modest ability to discriminate between women who will develop breast cancer and those who will not. Conclusion A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use. PMID:18316752

  14. Confidence scores for prediction models

    DEFF Research Database (Denmark)

    Gerds, Thomas Alexander; van de Wiel, MA

    2011-01-01

    In medical statistics, many alternative strategies are available for building a prediction model based on training data. Prediction models are routinely compared by means of their prediction performance in independent validation data. If only one data set is available for training and validation,...

  15. Multisite external validation of a risk prediction model for the diagnosis of blood stream infections in febrile pediatric oncology patients without severe neutropenia.

    Science.gov (United States)

    Esbenshade, Adam J; Zhao, Zhiguo; Aftandilian, Catherine; Saab, Raya; Wattier, Rachel L; Beauchemin, Melissa; Miller, Tamara P; Wilkes, Jennifer J; Kelly, Michael J; Fernbach, Alison; Jeng, Michael; Schwartz, Cindy L; Dvorak, Christopher C; Shyr, Yu; Moons, Karl G M; Sulis, Maria-Luisa; Friedman, Debra L

    2017-10-01

    Pediatric oncology patients are at an increased risk of invasive bacterial infection due to immunosuppression. The risk of such infection in the absence of severe neutropenia (absolute neutrophil count ≥ 500/μL) is not well established and a validated prediction model for blood stream infection (BSI) risk offers clinical usefulness. A 6-site retrospective external validation was conducted using a previously published risk prediction model for BSI in febrile pediatric oncology patients without severe neutropenia: the Esbenshade/Vanderbilt (EsVan) model. A reduced model (EsVan2) excluding 2 less clinically reliable variables also was created using the initial EsVan model derivative cohort, and was validated using all 5 external validation cohorts. One data set was used only in sensitivity analyses due to missing some variables. From the 5 primary data sets, there were a total of 1197 febrile episodes and 76 episodes of bacteremia. The overall C statistic for predicting bacteremia was 0.695, with a calibration slope of 0.50 for the original model and a calibration slope of 1.0 when recalibration was applied to the model. The model performed better in predicting high-risk bacteremia (gram-negative or Staphylococcus aureus infection) versus BSI alone, with a C statistic of 0.801 and a calibration slope of 0.65. The EsVan2 model outperformed the EsVan model across data sets with a C statistic of 0.733 for predicting BSI and a C statistic of 0.841 for high-risk BSI. The results of this external validation demonstrated that the EsVan and EsVan2 models are able to predict BSI across multiple performance sites and, once validated and implemented prospectively, could assist in decision making in clinical practice. Cancer 2017;123:3781-3790. © 2017 American Cancer Society. © 2017 American Cancer Society.

  16. Validation and sensitivity analysis of a two zone Diesel engine model for combustion and emissions prediction

    International Nuclear Information System (INIS)

    Rakopoulos, C.D.; Rakopoulos, D.C.; Giakoumis, E.G.; Kyritsis, D.C.

    2004-01-01

    The present two zone model of a direct injection (DI) Diesel engine divides the cylinder contents into a non-burning zone of air and another homogeneous zone in which fuel is continuously supplied from the injector and burned with entrained air from the air zone. The growth of the fuel spray zone, which comprises a number of fuel-air conical jets equal to the injector nozzle holes, is carefully modelled by incorporating jet mixing, thus determining the amount of oxygen available for combustion. The mass, energy and state equations are applied in each of the two zones to yield local temperatures and cylinder pressure histories. The concentration of the various constituents in the exhaust gases are calculated by adopting a chemical equilibrium scheme for the C-H-O system of the 11 species considered, together with chemical rate equations for the calculation of nitric oxide (NO). A model for evaluation of the soot formation and oxidation rates is included. The theoretical results from the relevant computer program are compared very favourably with the measurements from an experimental investigation conducted on a fully automated test bed, standard 'Hydra', DI Diesel engine installed at the authors' laboratory. In-cylinder pressure and temperature histories, nitric oxide concentration and soot density are among the interesting quantities tested for various loads and injection timings. As revealed, the model is sensitive to the selection of the constants of the fuel preparation and reaction sub-models, so that a relevant sensitivity analysis is undertaken. This leads to a better understanding of the physical mechanisms governed by these constants and also paves the way for construction of a reliable and relatively simple multi-zone model, which incorporates in each zone (packet) the philosophy of the present two zone model

  17. Validation and sensitivity analysis of a two zone diesel engine model for combustion and emissions prediction

    Energy Technology Data Exchange (ETDEWEB)

    Rakopoulos, C.D.; Rakopoulos, D.C.; Giakoumis, E.G. [National Technical University of Athens (Greece). Mechanical Engineering Dept.; Kyritsis, D.C. [University of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Mechanical and Industrial Engineering

    2004-06-01

    The present two zone model of a direct injection (DI) diesel engine divides the cylinder contents into a non-burning zone of air and another homogeneous zone in which fuel is continuously supplied from the injector and burned with entrained air from the air zone. The growth of the fuel spray zone, which comprises a number of fuel-air conical jets equal to the injector nozzle holes, is carefully modelled by incorporating jet mixing, thus determining the amount of oxygen available for combustion. The mass, energy and state equations are applied in each of the two zones to yield local temperatures and cylinder pressure histories. The concentration of the various constituents in the exhaust gases are calculated by adopting a chemical equilibrium scheme for the C-H-O system of the 11 species considered, together with chemical rate equations for the calculation of nitric oxide (NO). A model for evaluation of the soot formation and oxidation rates is included. The theoretical results from the relevant computer program are compared very favourably with the measurements from an experimental investigation conducted on a fully automated test bed, standard ''Hydra'', DI diesel engine installed at the authors' laboratory. In-cylinder pressure and temperature histories, nitric oxide concentration and soot density are among the interesting quantities tested for various loads and injection timings. As revealed, the model is sensitive to the selection of the constants of the fuel preparation and reaction sub-models, so that a relevant sensitivity analysis is undertaken. This leads to a better understanding of the physical mechanisms governed by these constants and also paves the way for construction of a reliable and relatively simple multi-zone model, which incorporates in each zone (packet) the philosophy of the present two zone model. (author)

  18. Development and internal validation of a prognostic model to predict recurrence free survival in patients with adult granulosa cell tumors of the ovary

    NARCIS (Netherlands)

    van Meurs, Hannah S.; Schuit, Ewoud; Horlings, Hugo M.; van der Velden, Jacobus; van Driel, Willemien J.; Mol, Ben Willem J.; Kenter, Gemma G.; Buist, Marrije R.

    2014-01-01

    Models to predict the probability of recurrence free survival exist for various types of malignancies, but a model for recurrence free survival in individuals with an adult granulosa cell tumor (GCT) of the ovary is lacking. We aimed to develop and internally validate such a prognostic model. We

  19. Validation of the AGDISP model for predicting airborne atrazine spray drift: a South African ground application case study

    CSIR Research Space (South Africa)

    Nsibande, SA

    2015-06-01

    Full Text Available Air dispersion software models for evaluating pesticide spray drift during application have been developed that can potentially serve as a cheaper convenient alternative to field monitoring campaigns. Such models require validation against field...

  20. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study

    NARCIS (Netherlands)

    K. ten Haaf (Kevin); J. Jeon (Jihyoun); M.C. Tammemagi (Martin); S.S. Han (Summer); C.Y. Kong (Chung Yin); S.K. Plevritis (Sylvia); E. Feuer (Eric); H.J. de Koning (Harry); E.W. Steyerberg (Ewout W.); R. Meza (Rafael)

    2017-01-01

    textabstractBackground: Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most

  1. Validation of a finite element human model for prediction of rib fractures

    NARCIS (Netherlands)

    Mordaka, J.K.; Meijer, R.; Rooij, L. van; Zmijewska, A.

    2007-01-01

    In the past, several crash test dummies were developed in order to measure forces acting on the human body during different loading conditions. However, they are limited in their biofidelity and their application type (frontal, lateral etc.). Recently, several numerical human models were developed.

  2. A BEM approach to validate a model for predicting sound propagation over non-flat terrain

    DEFF Research Database (Denmark)

    Quirósy Alpera, S.; Jacobsen, Finn; Juhl, P.M.

    2003-01-01

    to be discretised in the boundary element model. This Green's function is undefined for points below the impedance plane, and therefore valleys and hollows are taken into account by coupling the exterior domain above the ground with one or several interior domains below the ground, as suggested in a recent paper [J...

  3. Predictive validity of a non-induced mouse model of compulsive-like behavior

    NARCIS (Netherlands)

    Greene-Schloesser, D. M.; Van der Zee, E. A.; Sheppard, D. K.; Castillo, M. R.; Gregg, K. A.; Burrow, T.; Foltz, H.; Slater, M.; Bult-Ito, A.

    2011-01-01

    A key to advancing the understanding of obsessive-compulsive disorder (OCD)-like symptoms is the development of spontaneous animal models. Over 55 generations of bidirectional selection for nest-building behavior in house mice, Mus musculus, resulted in a 40-fold difference in the amount of cotton

  4. Validation of HEDR models

    International Nuclear Information System (INIS)

    Napier, B.A.; Simpson, J.C.; Eslinger, P.W.; Ramsdell, J.V. Jr.; Thiede, M.E.; Walters, W.H.

    1994-05-01

    The Hanford Environmental Dose Reconstruction (HEDR) Project has developed a set of computer models for estimating the possible radiation doses that individuals may have received from past Hanford Site operations. This document describes the validation of these models. In the HEDR Project, the model validation exercise consisted of comparing computational model estimates with limited historical field measurements and experimental measurements that are independent of those used to develop the models. The results of any one test do not mean that a model is valid. Rather, the collection of tests together provide a level of confidence that the HEDR models are valid

  5. VALIDITY OF GARBER MODEL IN PREDICTING PAVEMENT CONDITION INDEX OF FLEXIBLE PAVEMENT IN KERBALA CITY

    Directory of Open Access Journals (Sweden)

    Hussein A. Ewadh

    2018-05-01

    Full Text Available Pavement Condition Index (PCI is one of the important basics in pavement maintenance management system (PMMS, and it is used to evaluate the current and future pavement condition. This importantance in decision making to limit the maintenance needs, types of treatment, and maintenance priority. The aim of this research is to estimate the PCI value for flexible pavement urban roads in the study area (kerbala city by using Garber et al. developed model. Based on previous researches, data are collected for variables that have a significant impact on pavement condition. Data for pavement age (AGE, average daily traffic (ADT, and structural number (SN were collected for 44 sections in the network roads. A field survey (destructive test (core test and laboratory test (Marshall Test were used to determine the capacity of structure layer of pavement (SN. The condition index (CI output from a developed model was compared with the PCI output of PAVER 6.5.7 by using statistical analysis test. The developed model overestimates value of CI rather than PCI estimated from PAVER 6.5.7 due to statistical test to a 95% degree of confidence, (R = 0.771 for 44 sections (arterial and collector.

  6. Gas release during salt-well pumping: Model predictions and laboratory validation studies for soluble and insoluble gases

    International Nuclear Information System (INIS)

    Peurrung, L.M.; Caley, S.M.; Gauglitz, P.A.

    1997-08-01

    The Hanford Site has 149 single-shell tanks (SSTs) containing radioactive wastes that are complex mixes of radioactive and chemical products. Of these, 67 are known or suspected to have leaked liquid from the tanks into the surrounding soil. Salt-well pumping, or interim stabilization, is a well-established operation for removing drainable interstitial liquid from SSTs. The overall objective of this ongoing study is to develop a quantitative understanding of the release rates and cumulative releases of flammable gases from SSTs as a result of salt-well pumping. The current study is an extension of the previous work reported by Peurrung et al. (1996). The first objective of this current study was to conduct laboratory experiments to quantify the release of soluble and insoluble gases. The second was to determine experimentally the role of characteristic waste heterogeneities on the gas release rates. The third objective was to evaluate and validate the computer model STOMP (Subsurface Transport over Multiple Phases) used by Peurrung et al. (1996) to predict the release of both soluble (typically ammonia) and insoluble gases (typically hydrogen) during and after salt-well pumping. The fourth and final objective of the current study was to predict the gas release behavior for a range of typical tank conditions and actual tank geometry. In these models, the authors seek to include all the pertinent salt-well pumping operational parameters and a realistic range of physical properties of the SST wastes. For predicting actual tank behavior, two-dimensional (2-D) simulations were performed with a representative 2-D tank geometry

  7. Validation of a zero-dimensional model for prediction of NOx and engine performance for electronically controlled marine two-stroke diesel engines

    International Nuclear Information System (INIS)

    Scappin, Fabio; Stefansson, Sigurður H.; Haglind, Fredrik; Andreasen, Anders; Larsen, Ulrik

    2012-01-01

    The aim of this paper is to derive a methodology suitable for energy system analysis for predicting the performance and NO x emissions of marine low speed diesel engines. The paper describes a zero-dimensional model, evaluating the engine performance by means of an energy balance and a two zone combustion model using ideal gas law equations over a complete crank cycle. The combustion process is divided into intervals, and the product composition and flame temperature are calculated in each interval. The NO x emissions are predicted using the extended Zeldovich mechanism. The model is validated using experimental data from two MAN B and W engines; one case being data subject to engine parameter changes corresponding to simulating an electronically controlled engine; the second case providing data covering almost all model input and output parameters. The first case of validation suggests that the model can predict specific fuel oil consumption and NO x emissions within the 95% confidence intervals given by the experimental measurements. The second validation confirms the capability of the model to match measured engine output parameters based on measured engine input parameters with a maximum 5% deviation. - Highlights: ► A fast realistic model of a marine two-stroke low speed diesel engine was derived. ► The model is fast and accurate enough for future complex energy systems analysis. ► The effects of engine tuning were validated with experimental tests. ► The model was validated while constrained by experimental input and output data.

  8. Development and Validation of a Biodynamic Model for Mechanistically Predicting Metal Accumulation in Fish-Parasite Systems.

    Directory of Open Access Journals (Sweden)

    T T Yen Le

    Full Text Available Because of different reported effects of parasitism on the accumulation of metals in fish, it is important to consider parasites while interpreting bioaccumulation data from biomonitoring programmes. Accordingly, the first step is to take parasitism into consideration when simulating metal bioaccumulation in the fish host under laboratory conditions. In the present study, the accumulation of metals in fish-parasite systems was simulated by a one-compartment toxicokinetic model and compared to uninfected conspecifics. As such, metal accumulation in fish was assumed to result from a balance of different uptake and loss processes depending on the infection status. The uptake by parasites was considered an efflux from the fish host, similar to elimination. Physiological rate constants for the uninfected fish were parameterised based on the covalent index and the species weight while the parameterisation for the infected fish was carried out based on the reported effects of parasites on the uptake kinetics of the fish host. The model was then validated for the system of the chub Squalius cephalus and the acanthocephalan Pomphorhynchus tereticollis following 36-day exposure to waterborne Pb. The dissolved concentration of Pb in the exposure tank water fluctuated during the exposure, ranging from 40 to 120 μg/L. Generally, the present study shows that the one-compartment model can be an effective method for simulating the accumulation of metals in fish, taking into account effects of parasitism. In particular, the predicted concentrations of Cu, Fe, Zn, and Pb in the uninfected chub as well as in the infected chub and the acanthocephalans were within one order of magnitude of the measurements. The variation in the absorption efficiency and the elimination rate constant of the uninfected chub resulted in variations of about one order of magnitude in the predicted concentrations of Pb. Inclusion of further assumptions for simulating metal accumulation

  9. Preliminary validation of computational model for neutron flux prediction of Thai Research Reactor (TRR-1/M1)

    Science.gov (United States)

    Sabaibang, S.; Lekchaum, S.; Tipayakul, C.

    2015-05-01

    This study is a part of an on-going work to develop a computational model of Thai Research Reactor (TRR-1/M1) which is capable of accurately predicting the neutron flux level and spectrum. The computational model was created by MCNPX program and the CT (Central Thimble) in-core irradiation facility was selected as the location for validation. The comparison was performed with the typical flux measurement method routinely practiced at TRR-1/M1, that is, the foil activation technique. In this technique, gold foil is irradiated for a certain period of time and the activity of the irradiated target is measured to derive the thermal neutron flux. Additionally, the flux measurement with SPND (self-powered neutron detector) was also performed for comparison. The thermal neutron flux from the MCNPX simulation was found to be 1.79×1013 neutron/cm2s while that from the foil activation measurement was 4.68×1013 neutron/cm2s. On the other hand, the thermal neutron flux from the measurement using SPND was 2.47×1013 neutron/cm2s. An assessment of the differences among the three methods was done. The difference of the MCNPX with the foil activation technique was found to be 67.8% and the difference of the MCNPX with the SPND was found to be 27.8%.

  10. The derivation and validation of a simple model for predicting in-hospital mortality of acutely admitted patients to internal medicine wards.

    Science.gov (United States)

    Sakhnini, Ali; Saliba, Walid; Schwartz, Naama; Bisharat, Naiel

    2017-06-01

    Limited information is available about clinical predictors of in-hospital mortality in acute unselected medical admissions. Such information could assist medical decision-making.To develop a clinical model for predicting in-hospital mortality in unselected acute medical admissions and to test the impact of secondary conditions on hospital mortality.This is an analysis of the medical records of patients admitted to internal medicine wards at one university-affiliated hospital. Data obtained from the years 2013 to 2014 were used as a derivation dataset for creating a prediction model, while data from 2015 was used as a validation dataset to test the performance of the model. For each admission, a set of clinical and epidemiological variables was obtained. The main diagnosis at hospitalization was recorded, and all additional or secondary conditions that coexisted at hospital admission or that developed during hospital stay were considered secondary conditions.The derivation and validation datasets included 7268 and 7843 patients, respectively. The in-hospital mortality rate averaged 7.2%. The following variables entered the final model; age, body mass index, mean arterial pressure on admission, prior admission within 3 months, background morbidity of heart failure and active malignancy, and chronic use of statins and antiplatelet agents. The c-statistic (ROC-AUC) of the prediction model was 80.5% without adjustment for main or secondary conditions, 84.5%, with adjustment for the main diagnosis, and 89.5% with adjustment for the main diagnosis and secondary conditions. The accuracy of the predictive model reached 81% on the validation dataset.A prediction model based on clinical data with adjustment for secondary conditions exhibited a high degree of prediction accuracy. We provide a proof of concept that there is an added value for incorporating secondary conditions while predicting probabilities of in-hospital mortality. Further improvement of the model performance

  11. Development and validation of a preoperative prediction model for colorectal cancer T-staging based on MDCT images and clinical information.

    Science.gov (United States)

    Sa, Sha; Li, Jing; Li, Xiaodong; Li, Yongrui; Liu, Xiaoming; Wang, Defeng; Zhang, Huimao; Fu, Yu

    2017-08-15

    This study aimed to establish and evaluate the efficacy of a prediction model for colorectal cancer T-staging. T-staging was positively correlated with the level of carcinoembryonic antigen (CEA), expression of carbohydrate antigen 19-9 (CA19-9), wall deformity, blurred outer edges, fat infiltration, infiltration into the surrounding tissue, tumor size and wall thickness. Age, location, enhancement rate and enhancement homogeneity were negatively correlated with T-staging. The predictive results of the model were consistent with the pathological gold standard, and the kappa value was 0.805. The total accuracy of staging improved from 51.04% to 86.98% with the proposed model. The clinical, imaging and pathological data of 611 patients with colorectal cancer (419 patients in the training group and 192 patients in the validation group) were collected. A spearman correlation analysis was used to validate the relationship among these factors and pathological T-staging. A prediction model was trained with the random forest algorithm. T staging of the patients in the validation group was predicted by both prediction model and traditional method. The consistency, accuracy, sensitivity, specificity and area under the curve (AUC) were used to compare the efficacy of the two methods. The newly established comprehensive model can improve the predictive efficiency of preoperative colorectal cancer T-staging.

  12. Model Validation Status Review

    International Nuclear Information System (INIS)

    E.L. Hardin

    2001-01-01

    The primary objective for the Model Validation Status Review was to perform a one-time evaluation of model validation associated with the analysis/model reports (AMRs) containing model input to total-system performance assessment (TSPA) for the Yucca Mountain site recommendation (SR). This review was performed in response to Corrective Action Request BSC-01-C-01 (Clark 2001, Krisha 2001) pursuant to Quality Assurance review findings of an adverse trend in model validation deficiency. The review findings in this report provide the following information which defines the extent of model validation deficiency and the corrective action needed: (1) AMRs that contain or support models are identified, and conversely, for each model the supporting documentation is identified. (2) The use for each model is determined based on whether the output is used directly for TSPA-SR, or for screening (exclusion) of features, events, and processes (FEPs), and the nature of the model output. (3) Two approaches are used to evaluate the extent to which the validation for each model is compliant with AP-3.10Q (Analyses and Models). The approaches differ in regard to whether model validation is achieved within individual AMRs as originally intended, or whether model validation could be readily achieved by incorporating information from other sources. (4) Recommendations are presented for changes to the AMRs, and additional model development activities or data collection, that will remedy model validation review findings, in support of licensing activities. The Model Validation Status Review emphasized those AMRs that support TSPA-SR (CRWMS M and O 2000bl and 2000bm). A series of workshops and teleconferences was held to discuss and integrate the review findings. The review encompassed 125 AMRs (Table 1) plus certain other supporting documents and data needed to assess model validity. The AMRs were grouped in 21 model areas representing the modeling of processes affecting the natural and

  13. Model Validation Status Review

    Energy Technology Data Exchange (ETDEWEB)

    E.L. Hardin

    2001-11-28

    The primary objective for the Model Validation Status Review was to perform a one-time evaluation of model validation associated with the analysis/model reports (AMRs) containing model input to total-system performance assessment (TSPA) for the Yucca Mountain site recommendation (SR). This review was performed in response to Corrective Action Request BSC-01-C-01 (Clark 2001, Krisha 2001) pursuant to Quality Assurance review findings of an adverse trend in model validation deficiency. The review findings in this report provide the following information which defines the extent of model validation deficiency and the corrective action needed: (1) AMRs that contain or support models are identified, and conversely, for each model the supporting documentation is identified. (2) The use for each model is determined based on whether the output is used directly for TSPA-SR, or for screening (exclusion) of features, events, and processes (FEPs), and the nature of the model output. (3) Two approaches are used to evaluate the extent to which the validation for each model is compliant with AP-3.10Q (Analyses and Models). The approaches differ in regard to whether model validation is achieved within individual AMRs as originally intended, or whether model validation could be readily achieved by incorporating information from other sources. (4) Recommendations are presented for changes to the AMRs, and additional model development activities or data collection, that will remedy model validation review findings, in support of licensing activities. The Model Validation Status Review emphasized those AMRs that support TSPA-SR (CRWMS M&O 2000bl and 2000bm). A series of workshops and teleconferences was held to discuss and integrate the review findings. The review encompassed 125 AMRs (Table 1) plus certain other supporting documents and data needed to assess model validity. The AMRs were grouped in 21 model areas representing the modeling of processes affecting the natural and

  14. Non sentinel node involvement prediction for sentinel node micrometastases in breast cancer: nomogram validation and comparison with other models.

    Science.gov (United States)

    Houvenaeghel, Gilles; Bannier, Marie; Nos, Claude; Giard, Sylvia; Mignotte, Herve; Jacquemier, Jocelyne; Martino, Marc; Esterni, Benjamin; Belichard, Catherine; Classe, Jean-Marc; Tunon de Lara, Christine; Cohen, Monique; Payan, Raoul; Blanchot, Jerome; Rouanet, Philippe; Penault-Llorca, Frederique; Bonnier, Pascal; Fournet, Sandrine; Agostini, Aubert; Marchal, Frederique; Garbay, Jean-Remi

    2012-04-01

    The risk of non sentinel node (NSN) involvement varies in function of the characteristics of sentinel nodes (SN) and primary tumor. Our aim was to determine and validate a statistical tool (a nomogram) able to predict the risk of NSN involvement in case of SN micro or sub-micrometastasis of breast cancer. We have compared this monogram with other models described in the literature. We have collected data on 905 patients, then 484 other patients, to build and validate the nomogram and compare it with other published scores and nomograms. Multivariate analysis conducted on the data of the first cohort allowed us to define a nomogram based on 5 criteria: the method of SN detection (immunohistochemistry or by standard coloration with HES); the ratio of positive SN out of total removed SN; the pathologic size of the tumor; the histological type; and the presence (or not) of lympho-vascular invasion. The nomogram developed here is the only one dedicated to micrometastasis and developed on the basis of two large cohorts. The results of this statistical tool in the calculation of the risk of NSN involvement is similar to those of the MSKCC (the similarly more effective nomogram according to the literature), with a lower rate of false negatives. this nomogram is dedicated specifically to cases of SN involvement by metastasis lower or equal to 2 mm. It could be used in clinical practice in the way to omit ALND when the risk of NSN involvement is low. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes.

    Science.gov (United States)

    Zhao, Lue Ping; Carlsson, Annelie; Larsson, Helena Elding; Forsander, Gun; Ivarsson, Sten A; Kockum, Ingrid; Ludvigsson, Johnny; Marcus, Claude; Persson, Martina; Samuelsson, Ulf; Örtqvist, Eva; Pyo, Chul-Woo; Bolouri, Hamid; Zhao, Michael; Nelson, Wyatt C; Geraghty, Daniel E; Lernmark, Åke

    2017-11-01

    It is of interest to predict possible lifetime risk of type 1 diabetes (T1D) in young children for recruiting high-risk subjects into longitudinal studies of effective prevention strategies. Utilizing a case-control study in Sweden, we applied a recently developed next generation targeted sequencing technology to genotype class II genes and applied an object-oriented regression to build and validate a prediction model for T1D. In the training set, estimated risk scores were significantly different between patients and controls (P = 8.12 × 10 -92 ), and the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was 0.917. Using the validation data set, we validated the result with AUC of 0.886. Combining both training and validation data resulted in a predictive model with AUC of 0.903. Further, we performed a "biological validation" by correlating risk scores with 6 islet autoantibodies, and found that the risk score was significantly correlated with IA-2A (Z-score = 3.628, P < 0.001). When applying this prediction model to the Swedish population, where the lifetime T1D risk ranges from 0.5% to 2%, we anticipate identifying approximately 20 000 high-risk subjects after testing all newborns, and this calculation would identify approximately 80% of all patients expected to develop T1D in their lifetime. Through both empirical and biological validation, we have established a prediction model for estimating lifetime T1D risk, using class II HLA. This prediction model should prove useful for future investigations to identify high-risk subjects for prevention research in high-risk populations. Copyright © 2017 John Wiley & Sons, Ltd.

  16. Validation of MCNP and ORIGEN-S 3-D computational model for reactivity predictions during BR2 operation

    International Nuclear Information System (INIS)

    Kalcheva, S.; Koonen, E.; Ponsard, B.

    2005-01-01

    The Belgian Material Test Reactor (MTR) BR2 is strongly heterogeneous high flux engineering test reactor at SCK-CEN (Centre d'Etude de l'energie Nucleaire) in Mol at a thermal power 60 to 100 MW. It deploys highly enriched uranium, water cooled concentric plate fuel elements, positioned inside a beryllium reflector with complex hyperboloid arrangement of test holes. The objective of this paper is the validation of a MCNP and ORIGEN-S 3D model for reactivity predictions of the entire BR2 core during reactor operation. We employ the Monte Carlo code MCNP-4C for evaluating the effective multiplication factor k eff and 3D space dependent specific power distribution. The 1D code ORIGEN-S is used for calculation of isotopic fuel depletion versus burn up and preparation of a database (DB) with depleted fuel compositions. The approach taken is to evaluate the 3D power distribution at each time step and along with DB to evaluate the 3D isotopic fuel depletion at the next step and to deduce the corresponding shim rods positions of the reactor operation. The capabilities of the both codes are fully exploited without constraints on the number of involved isotope depletion chains or increase of the computational time. The reactor has a complex operation, with important shutdowns between cycles, and its reactivity is strongly influenced by poisons, mainly 3 He and 6 Li from the beryllium reflector, and burnable absorbers 149 Sm and 10 B in the fresh UAlx fuel. Our computational predictions for the shim rods position at various restarts are within 0.5$ (β eff =0.0072). (author)

  17. Validation of simulation models

    DEFF Research Database (Denmark)

    Rehman, Muniza; Pedersen, Stig Andur

    2012-01-01

    In philosophy of science, the interest for computational models and simulations has increased heavily during the past decades. Different positions regarding the validity of models have emerged but the views have not succeeded in capturing the diversity of validation methods. The wide variety...

  18. Validation for global solar wind prediction using Ulysses comparison: Multiple coronal and heliospheric models installed at the Community Coordinated Modeling Center

    Science.gov (United States)

    Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

    2016-08-01

    The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, we have quantitatively assessed the models' capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs. The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

  19. Validation for Global Solar Wind Prediction Using Ulysses Comparison: Multiple Coronal and Heliospheric Models Installed at the Community Coordinated Modeling Center

    Science.gov (United States)

    Jian, L. K.; MacNeice, P. J.; Mays, M. L.; Taktakishvili, A.; Odstrcil, D.; Jackson, B.; Yu, H.-S.; Riley, P.; Sokolov, I. V.

    2016-01-01

    The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and Heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our third-party validation from the previous near-Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, wehave quantitatively assessed the models capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSA-Enlil model results vary with three different magnetogram inputs.The MAS-Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillation-tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.

  20. A biodynamic model predicting waterborne lead bioaccumulation in Gammarus pulex: Influence of water chemistry and in situ validation

    International Nuclear Information System (INIS)

    Urien, N.; Uher, E.; Billoir, E.; Geffard, O.; Fechner, L.C.; Lebrun, J.D.

    2015-01-01

    Metals bioaccumulated in aquatic organisms are considered to be a good indicator of bioavailable metal contamination levels in freshwaters. However, bioaccumulation depends on the metal, the species, and the water chemistry that influences metal bioavailability. In the laboratory, a kinetic model was used to describe waterborne Pb bioaccumulated in Gammarus pulex. Uptake and elimination rate constants were successfully determined and the effect of Ca 2+ on Pb uptake was integrated into the model. Thereafter, accumulated Pb concentrations in organisms were predicted with the model and compared with those measured in native populations from the Seine watershed (France). The predictions had a good agreement with the bioaccumulation levels observed in native gammarids and particularly when the effect of calcium was considered. To conclude, kinetic parameters experimentally derived for Pb in G. pulex are applicable in environmental conditions. Moreover, the consideration of the water's chemistry is crucial for a reliable interpretation of bioaccumulation. - Highlights: • Kinetic model was used to describe waterborne Pb bioaccumulation in G. pulex. • Ca 2+ inhibits Pb uptake by G. pulex in the laboratory. • Model predictions were compared to bioaccumulated Pb in native G. pulex. • Model accurately predicts waterborne bioaccumulated Pb in gammarids. • Considering the influence of Ca 2+ improves the model predictions in the field. - An experimentally-derived kinetic model considering the effect of calcium was relevant to predict the waterborne Pb bioaccumulation in native Gammarus pulex

  1. The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery.

    Science.gov (United States)

    Glance, Laurent G; Lustik, Stewart J; Hannan, Edward L; Osler, Turner M; Mukamel, Dana B; Qian, Feng; Dick, Andrew W

    2012-04-01

    To develop a 30-day mortality risk index for noncardiac surgery that can be used to communicate risk information to patients and guide clinical management at the "point-of-care," and that can be used by surgeons and hospitals to internally audit their quality of care. Clinicians rely on the Revised Cardiac Risk Index to quantify the risk of cardiac complications in patients undergoing noncardiac surgery. Because mortality from noncardiac causes accounts for many perioperative deaths, there is also a need for a simple bedside risk index to predict 30-day all-cause mortality after noncardiac surgery. Retrospective cohort study of 298,772 patients undergoing noncardiac surgery during 2005 to 2007 using the American College of Surgeons National Surgical Quality Improvement Program database. The 9-point S-MPM (Surgical Mortality Probability Model) 30-day mortality risk index was derived empirically and includes three risk factors: ASA (American Society of Anesthesiologists) physical status, emergency status, and surgery risk class. Patients with ASA physical status I, II, III, IV or V were assigned either 0, 2, 4, 5, or 6 points, respectively; intermediate- or high-risk procedures were assigned 1 or 2 points, respectively; and emergency procedures were assigned 1 point. Patients with risk scores less than 5 had a predicted risk of mortality less than 0.50%, whereas patients with a risk score of 5 to 6 had a risk of mortality between 1.5% and 4.0%. Patients with a risk score greater than 6 had risk of mortality more than 10%. S-MPM exhibited excellent discrimination (C statistic, 0.897) and acceptable calibration (Hosmer-Lemeshow statistic 13.0, P = 0.023) in the validation data set. Thirty-day mortality after noncardiac surgery can be accurately predicted using a simple and accurate risk score based on information readily available at the bedside. This risk index may play a useful role in facilitating shared decision making, developing and implementing risk

  2. Genome-Wide Association Studies and Comparison of Models and Cross-Validation Strategies for Genomic Prediction of Quality Traits in Advanced Winter Wheat Breeding Lines

    Directory of Open Access Journals (Sweden)

    Peter S. Kristensen

    2018-02-01

    Full Text Available The aim of the this study was to identify SNP markers associated with five important wheat quality traits (grain protein content, Zeleny sedimentation, test weight, thousand-kernel weight, and falling number, and to investigate the predictive abilities of GBLUP and Bayesian Power Lasso models for genomic prediction of these traits. In total, 635 winter wheat lines from two breeding cycles in the Danish plant breeding company Nordic Seed A/S were phenotyped for the quality traits and genotyped for 10,802 SNPs. GWAS were performed using single marker regression and Bayesian Power Lasso models. SNPs with large effects on Zeleny sedimentation were found on chromosome 1B, 1D, and 5D. However, GWAS failed to identify single SNPs with significant effects on the other traits, indicating that these traits were controlled by many QTL with small effects. The predictive abilities of the models for genomic prediction were studied using different cross-validation strategies. Leave-One-Out cross-validations resulted in correlations between observed phenotypes corrected for fixed effects and genomic estimated breeding values of 0.50 for grain protein content, 0.66 for thousand-kernel weight, 0.70 for falling number, 0.71 for test weight, and 0.79 for Zeleny sedimentation. Alternative cross-validations showed that the genetic relationship between lines in training and validation sets had a bigger impact on predictive abilities than the number of lines included in the training set. Using Bayesian Power Lasso instead of GBLUP models, gave similar or slightly higher predictive abilities. Genomic prediction based on all SNPs was more effective than prediction based on few associated SNPs.

  3. Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient.

    Science.gov (United States)

    Chirico, Nicola; Gramatica, Paola

    2011-09-26

    The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient Q(2)(F1) (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r(2)(m) (Roy), Q(2)(F2) (Schüürmann et al.), Q(2)(F3) (Consonni et al.). In QSAR studies these measures are usually in accordance, though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive.

  4. Predicting germination in semi-arid wildland seedbeds II. Field validation of wet thermal-time models

    Science.gov (United States)

    Jennifer K. Rawlins; Bruce A. Roundy; Dennis Eggett; Nathan. Cline

    2011-01-01

    Accurate prediction of germination for species used for semi-arid land revegetation would support selection of plant materials for specific climatic conditions and sites. Wet thermal-time models predict germination time by summing progress toward germination subpopulation percentages as a function of temperature across intermittent wet periods or within singular wet...

  5. Validating the Malheur model for predicting ponderosa pine post-fire mortality using 24 fires in the Pacific Northwest, USA

    Science.gov (United States)

    Walter G. Thies; Douglas J. Westlind

    2012-01-01

    Fires, whether intentionally or accidentally set, commonly occur in western interior forests of the US. Following fire, managers need the ability to predict mortality of individual trees based on easily observed characteristics. Previously, a two-factor model using crown scorch and bole scorch proportions was developed with data from 3415 trees for predicting the...

  6. Predicting retroperitoneal histology in postchemotherapy testicular germ cell cancer : A model update and multicentre validation with more than 1000 patients

    NARCIS (Netherlands)

    Vergouwe, Yvonne; Steyerberg, Ewout W.; Foster, Richard S.; Sleijfer, Dirk T.; Fossa, Sophie D.; Gerl, Arthur; de Wit, Ronald; Roberts, J. Trevor; Habbema, J. Dik F.

    Objectives: Surgical resection of postchemotherapy retroperitoneal lymph nodes is often performed in patients with advanced nonseminomatous testicular germ cell cancer. We previously developed a model to predict the probability that the lymph nodes contain only necrotic or fibrotic (benign) tissue

  7. Five year experience in management of perforated peptic ulcer and validation of common mortality risk prediction models - are existing models sufficient? A retrospective cohort study.

    Science.gov (United States)

    Anbalakan, K; Chua, D; Pandya, G J; Shelat, V G

    2015-02-01

    Emergency surgery for perforated peptic ulcer (PPU) is associated with significant morbidity and mortality. Accurate and early risk stratification is important. The primary aim of this study is to validate the various existing MRPMs and secondary aim is to audit our experience of managing PPU. 332 patients who underwent emergency surgery for PPU at a single intuition from January 2008 to December 2012 were studied. Clinical and operative details were collected. Four MRPMs: American Society of Anesthesiology (ASA) score, Boey's score, Mannheim peritonitis index (MPI) and Peptic ulcer perforation (PULP) score were validated. Median age was 54.7 years (range 17-109 years) with male predominance (82.5%). 61.7% presented within 24 h of onset of abdominal pain. Median length of stay was 7 days (range 2-137 days). Intra-abdominal collection, leakage, re-operation and 30-day mortality rates were 8.1%, 2.1%, 1.2% and 7.2% respectively. All the four MRPMs predicted intra-abdominal collection and mortality; however, only MPI predicted leak (p = 0.01) and re-operation (p = 0.02) rates. The area under curve for predicting mortality was 75%, 72%, 77.2% and 75% for ASA score, Boey's score, MPI and PULP score respectively. Emergency surgery for PPU has low morbidity and mortality in our experience. MPI is the only scoring system which predicts all - intra-abdominal collection, leak, reoperation and mortality. All four MRPMs had a similar and fair accuracy to predict mortality, however due to geographic and demographic diversity and inherent weaknesses of exiting MRPMs, quest for development of an ideal model should continue. Copyright © 2015 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

  8. Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models

    Directory of Open Access Journals (Sweden)

    Testa Antonia C

    2010-10-01

    Full Text Available Abstract Background Hitherto, risk prediction models for preoperative ultrasound-based diagnosis of ovarian tumors were dichotomous (benign versus malignant. We develop and validate polytomous models (models that predict more than two events to diagnose ovarian tumors as benign, borderline, primary invasive or metastatic invasive. The main focus is on how different types of models perform and compare. Methods A multi-center dataset containing 1066 women was used for model development and internal validation, whilst another multi-center dataset of 1938 women was used for temporal and external validation. Models were based on standard logistic regression and on penalized kernel-based algorithms (least squares support vector machines and kernel logistic regression. We used true polytomous models as well as combinations of dichotomous models based on the 'pairwise coupling' technique to produce polytomous risk estimates. Careful variable selection was performed, based largely on cross-validated c-index estimates. Model performance was assessed with the dichotomous c-index (i.e. the area under the ROC curve and a polytomous extension, and with calibration graphs. Results For all models, between 9 and 11 predictors were selected. Internal validation was successful with polytomous c-indexes between 0.64 and 0.69. For the best model dichotomous c-indexes were between 0.73 (primary invasive vs metastatic and 0.96 (borderline vs metastatic. On temporal and external validation, overall discrimination performance was good with polytomous c-indexes between 0.57 and 0.64. However, discrimination between primary and metastatic invasive tumors decreased to near random levels. Standard logistic regression performed well in comparison with advanced algorithms, and combining dichotomous models performed well in comparison with true polytomous models. The best model was a combination of dichotomous logistic regression models. This model is available online

  9. Right Heart End-Systolic Remodeling Index Strongly Predicts Outcomes in Pulmonary Arterial Hypertension: Comparison With Validated Models.

    Science.gov (United States)

    Amsallem, Myriam; Sweatt, Andrew J; Aymami, Marie C; Kuznetsova, Tatiana; Selej, Mona; Lu, HongQuan; Mercier, Olaf; Fadel, Elie; Schnittger, Ingela; McConnell, Michael V; Rabinovitch, Marlene; Zamanian, Roham T; Haddad, Francois

    2017-06-01

    Right ventricular (RV) end-systolic dimensions provide information on both size and function. We investigated whether an internally scaled index of end-systolic dimension is incremental to well-validated prognostic scores in pulmonary arterial hypertension. From 2005 to 2014, 228 patients with pulmonary arterial hypertension were prospectively enrolled. RV end-systolic remodeling index (RVESRI) was defined by lateral length divided by septal height. The incremental values of RV free wall longitudinal strain and RVESRI to risk scores were determined. Mean age was 49±14 years, 78% were female, 33% had connective tissue disease, 52% were in New York Heart Association class ≥III, and mean pulmonary vascular resistance was 11.2±6.4 WU. RVESRI and right atrial area were strongly connected to the other right heart metrics. Three zones of adaptation (adapted, maladapted, and severely maladapted) were identified based on the RVESRI to RV systolic pressure relationship. During a mean follow-up of 3.9±2.4 years, the primary end point of death, transplant, or admission for heart failure was reached in 88 patients. RVESRI was incremental to risk prediction scores in pulmonary arterial hypertension, including the Registry to Evaluate Early and Long-Term PAH Disease Management score, the Pulmonary Hypertension Connection equation, and the Mayo Clinic model. Using multivariable analysis, New York Heart Association class III/IV, RVESRI, and log NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) were retained (χ 2 , 62.2; P right heart metrics, RVESRI demonstrated the best test-retest characteristics. RVESRI is a simple reproducible prognostic marker in patients with pulmonary arterial hypertension. © 2017 American Heart Association, Inc.

  10. Predicting Overall Survival After Stereotactic Ablative Radiation Therapy in Early-Stage Lung Cancer: Development and External Validation of the Amsterdam Prognostic Model

    Energy Technology Data Exchange (ETDEWEB)

    Louie, Alexander V., E-mail: Dr.alexlouie@gmail.com [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, Massachusetts (United States); Haasbeek, Cornelis J.A. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Mokhles, Sahar [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Rodrigues, George B. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Stephans, Kevin L. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Lagerwaard, Frank J. [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands); Palma, David A. [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Videtic, Gregory M.M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Warner, Andrew [Department of Radiation Oncology, London Regional Cancer Program, University of Western Ontario, London, Ontario (Canada); Takkenberg, Johanna J.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Reddy, Chandana A. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Maat, Alex P.W.M. [Department of Cardio-Thoracic Surgery, Erasmus University Medical Center, Rotterdam (Netherlands); Woody, Neil M. [Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio (United States); Slotman, Ben J.; Senan, Suresh [Department of Radiation Oncology, VU University Medical Center, Amsterdam (Netherlands)

    2015-09-01

    Purpose: A prognostic model for 5-year overall survival (OS), consisting of recursive partitioning analysis (RPA) and a nomogram, was developed for patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR). Methods and Materials: A primary dataset of 703 ES-NSCLC SABR patients was randomly divided into a training (67%) and an internal validation (33%) dataset. In the former group, 21 unique parameters consisting of patient, treatment, and tumor factors were entered into an RPA model to predict OS. Univariate and multivariate models were constructed for RPA-selected factors to evaluate their relationship with OS. A nomogram for OS was constructed based on factors significant in multivariate modeling and validated with calibration plots. Both the RPA and the nomogram were externally validated in independent surgical (n=193) and SABR (n=543) datasets. Results: RPA identified 2 distinct risk classes based on tumor diameter, age, World Health Organization performance status (PS) and Charlson comorbidity index. This RPA had moderate discrimination in SABR datasets (c-index range: 0.52-0.60) but was of limited value in the surgical validation cohort. The nomogram predicting OS included smoking history in addition to RPA-identified factors. In contrast to RPA, validation of the nomogram performed well in internal validation (r{sup 2}=0.97) and external SABR (r{sup 2}=0.79) and surgical cohorts (r{sup 2}=0.91). Conclusions: The Amsterdam prognostic model is the first externally validated prognostication tool for OS in ES-NSCLC treated with SABR available to individualize patient decision making. The nomogram retained strong performance across surgical and SABR external validation datasets. RPA performance was poor in surgical patients, suggesting that 2 different distinct patient populations are being treated with these 2 effective modalities.

  11. Prediction and validation of pool fire development in enclosures by means of CFD Models for risk assessment of nuclear power plants (Poolfire) - Report year 2

    International Nuclear Information System (INIS)

    Van Hees, P.; Wahlqvist, J.; Kong, D.; Hostikka, S.; Sikanen, T.; Husted, B.; Magnusson, T.; Joerud, F.

    2013-05-01

    Fires in nuclear power plants can be an important hazard for the overall safety of the facility. One of the typical fire sources is a pool fire. It is therefore important to have good knowledge on the fire behaviour of pool fire and be able to predict the heat release rate by prediction of the mass loss rate. This project envisages developing a pyrolysis model to be used in CFD models. In this report the activities for second year are reported, which is an overview of the experiments conducted, further development and validation of models and cases study to be selected in year 3. (Author)

  12. Prediction and validation of pool fire development in enclosures by means of CFD Models for risk assessment of nuclear power plants (Poolfire) - Report year 2

    Energy Technology Data Exchange (ETDEWEB)

    van Hees, P.; Wahlqvist, J.; Kong, D. [Lund Univ., Lund (Sweden); Hostikka, S.; Sikanen, T. [VTT Technical Research Centre of Finland (Finland); Husted, B. [Haugesund Univ. College, Stord (Norway); Magnusson, T. [Ringhals AB, Vaeroebacka (Sweden); Joerud, F. [European Spallation Source (ESS), Lund (Sweden)

    2013-05-15

    Fires in nuclear power plants can be an important hazard for the overall safety of the facility. One of the typical fire sources is a pool fire. It is therefore important to have good knowledge on the fire behaviour of pool fire and be able to predict the heat release rate by prediction of the mass loss rate. This project envisages developing a pyrolysis model to be used in CFD models. In this report the activities for second year are reported, which is an overview of the experiments conducted, further development and validation of models and cases study to be selected in year 3. (Author)

  13. Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center.

    Science.gov (United States)

    Neal, Andrew; Hannah, Sam; Sanderson, Penelope; Bolland, Scott; Mooij, Martijn; Murphy, Sean

    2014-03-01

    The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. A multilevel workload model was developed in Study I with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters.The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs.Tactical uses include the dynamic reallocation of resources to meet changes in demand.

  14. Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial

    NARCIS (Netherlands)

    Wille, M.M.W.; Riel, S.J. van; Saghir, Z.; Dirksen, A.; Pedersen, J.H.; Jacobs, C.; Thomsen, L.H.u.; Scholten, E.T.; Skovgaard, L.T.; Ginneken, B. van

    2015-01-01

    Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models.From the DLCST database, 1,152

  15. A Validated Clinical Risk Prediction Model for Lung Cancer in Smokers of All Ages and Exposure Types

    DEFF Research Database (Denmark)

    Markaki, Maria; Tsamardinos, Ioannis; Langhammer, Arnulf

    2018-01-01

    Lung cancer causes >1·6 million deaths annually, with early diagnosis being paramount to effective treatment. Here we present a validated risk assessment model for lung cancer screening. The prospective HUNT2 population study in Norway examined 65,237 people aged >20years in 1995-97. After a median...

  16. Liver stiffness value-based risk estimation of late recurrence after curative resection of hepatocellular carcinoma: development and validation of a predictive model.

    Directory of Open Access Journals (Sweden)

    Kyu Sik Jung

    Full Text Available Preoperative liver stiffness (LS measurement using transient elastography (TE is useful for predicting late recurrence after curative resection of hepatocellular carcinoma (HCC. We developed and validated a novel LS value-based predictive model for late recurrence of HCC.Patients who were due to undergo curative resection of HCC between August 2006 and January 2010 were prospectively enrolled and TE was performed prior to operations by study protocol. The predictive model of late recurrence was constructed based on a multiple logistic regression model. Discrimination and calibration were used to validate the model.Among a total of 139 patients who were finally analyzed, late recurrence occurred in 44 patients, with a median follow-up of 24.5 months (range, 12.4-68.1. We developed a predictive model for late recurrence of HCC using LS value, activity grade II-III, presence of multiple tumors, and indocyanine green retention rate at 15 min (ICG R15, which showed fairly good discrimination capability with an area under the receiver operating characteristic curve (AUROC of 0.724 (95% confidence intervals [CIs], 0.632-0.816. In the validation, using a bootstrap method to assess discrimination, the AUROC remained largely unchanged between iterations, with an average AUROC of 0.722 (95% CIs, 0.718-0.724. When we plotted a calibration chart for predicted and observed risk of late recurrence, the predicted risk of late recurrence correlated well with observed risk, with a correlation coefficient of 0.873 (P<0.001.A simple LS value-based predictive model could estimate the risk of late recurrence in patients who underwent curative resection of HCC.

  17. Development and validation of a combined phased acoustical radiosity and image source model for predicting sound fields in rooms

    DEFF Research Database (Denmark)

    Marbjerg, Gerd Høy; Brunskog, Jonas; Jeong, Cheol-Ho

    2015-01-01

    A model, combining acoustical radiosity and the image source method, including phase shifts on reflection, has been developed. The model is denoted Phased Acoustical Radiosity and Image Source Method (PARISM), and it has been developed in order to be able to model both specular and diffuse...... radiosity by regarding the model as being stochastic. Three methods of implementation are proposed and investigated, and finally, recommendations are made for their use. Validation of the image source method is done by comparison with finite element simulations of a rectangular room with a porous absorber...

  18. Groundwater Model Validation

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed E. Hassan

    2006-01-24

    Models have an inherent uncertainty. The difficulty in fully characterizing the subsurface environment makes uncertainty an integral component of groundwater flow and transport models, which dictates the need for continuous monitoring and improvement. Building and sustaining confidence in closure decisions and monitoring networks based on models of subsurface conditions require developing confidence in the models through an iterative process. The definition of model validation is postulated as a confidence building and long-term iterative process (Hassan, 2004a). Model validation should be viewed as a process not an end result. Following Hassan (2004b), an approach is proposed for the validation process of stochastic groundwater models. The approach is briefly summarized herein and detailed analyses of acceptance criteria for stochastic realizations and of using validation data to reduce input parameter uncertainty are presented and applied to two case studies. During the validation process for stochastic models, a question arises as to the sufficiency of the number of acceptable model realizations (in terms of conformity with validation data). Using a hierarchical approach to make this determination is proposed. This approach is based on computing five measures or metrics and following a decision tree to determine if a sufficient number of realizations attain satisfactory scores regarding how they represent the field data used for calibration (old) and used for validation (new). The first two of these measures are applied to hypothetical scenarios using the first case study and assuming field data consistent with the model or significantly different from the model results. In both cases it is shown how the two measures would lead to the appropriate decision about the model performance. Standard statistical tests are used to evaluate these measures with the results indicating they are appropriate measures for evaluating model realizations. The use of validation

  19. External validation and clinical utility of a prediction model for 6-month mortality in patients undergoing hemodialysis for end-stage kidney disease.

    Science.gov (United States)

    Forzley, Brian; Er, Lee; Chiu, Helen Hl; Djurdjev, Ognjenka; Martinusen, Dan; Carson, Rachel C; Hargrove, Gaylene; Levin, Adeera; Karim, Mohamud

    2018-02-01

    End-stage kidney disease is associated with poor prognosis. Health care professionals must be prepared to address end-of-life issues and identify those at high risk for dying. A 6-month mortality prediction model for patients on dialysis derived in the United States is used but has not been externally validated. We aimed to assess the external validity and clinical utility in an independent cohort in Canada. We examined the performance of the published 6-month mortality prediction model, using discrimination, calibration, and decision curve analyses. Data were derived from a cohort of 374 prevalent dialysis patients in two regions of British Columbia, Canada, which included serum albumin, age, peripheral vascular disease, dementia, and answers to the "the surprise question" ("Would I be surprised if this patient died within the next year?"). The observed mortality in the validation cohort was 11.5% at 6 months. The prediction model had reasonable discrimination (c-stat = 0.70) but poor calibration (calibration-in-the-large = -0.53 (95% confidence interval: -0.88, -0.18); calibration slope = 0.57 (95% confidence interval: 0.31, 0.83)) in our data. Decision curve analysis showed the model only has added value in guiding clinical decision in a small range of threshold probabilities: 8%-20%. Despite reasonable discrimination, the prediction model has poor calibration in this external study cohort; thus, it may have limited clinical utility in settings outside of where it was derived. Decision curve analysis clarifies limitations in clinical utility not apparent by receiver operating characteristic curve analysis. This study highlights the importance of external validation of prediction models prior to routine use in clinical practice.

  20. Verification, validation, and reliability of predictions

    International Nuclear Information System (INIS)

    Pigford, T.H.; Chambre, P.L.

    1987-04-01

    The objective of predicting long-term performance should be to make reliable determinations of whether the prediction falls within the criteria for acceptable performance. Establishing reliable predictions of long-term performance of a waste repository requires emphasis on valid theories to predict performance. The validation process must establish the validity of the theory, the parameters used in applying the theory, the arithmetic of calculations, and the interpretation of results; but validation of such performance predictions is not possible unless there are clear criteria for acceptable performance. Validation programs should emphasize identification of the substantive issues of prediction that need to be resolved. Examples relevant to waste package performance are predicting the life of waste containers and the time distribution of container failures, establishing the criteria for defining container failure, validating theories for time-dependent waste dissolution that depend on details of the repository environment, and determining the extent of congruent dissolution of radionuclides in the UO 2 matrix of spent fuel. Prediction and validation should go hand in hand and should be done and reviewed frequently, as essential tools for the programs to design and develop repositories. 29 refs

  1. Elements of a pragmatic approach for dealing with bias and uncertainty in experiments through predictions : experiment design and data conditioning; %22real space%22 model validation and conditioning; hierarchical modeling and extrapolative prediction.

    Energy Technology Data Exchange (ETDEWEB)

    Romero, Vicente Jose

    2011-11-01

    This report explores some important considerations in devising a practical and consistent framework and methodology for utilizing experiments and experimental data to support modeling and prediction. A pragmatic and versatile 'Real Space' approach is outlined for confronting experimental and modeling bias and uncertainty to mitigate risk in modeling and prediction. The elements of experiment design and data analysis, data conditioning, model conditioning, model validation, hierarchical modeling, and extrapolative prediction under uncertainty are examined. An appreciation can be gained for the constraints and difficulties at play in devising a viable end-to-end methodology. Rationale is given for the various choices underlying the Real Space end-to-end approach. The approach adopts and refines some elements and constructs from the literature and adds pivotal new elements and constructs. Crucially, the approach reflects a pragmatism and versatility derived from working many industrial-scale problems involving complex physics and constitutive models, steady-state and time-varying nonlinear behavior and boundary conditions, and various types of uncertainty in experiments and models. The framework benefits from a broad exposure to integrated experimental and modeling activities in the areas of heat transfer, solid and structural mechanics, irradiated electronics, and combustion in fluids and solids.

  2. Development and validation of a prediction model for long-term sickness absence based on occupational health survey variables

    DEFF Research Database (Denmark)

    Roelen, Corné; Thorsen, Sannie; Heymans, Martijn

    2018-01-01

    LTSA during follow-up. Results: The 15-predictor model was reduced to a 9-predictor model including age, gender, education, self-rated health, mental health, prior LTSA, work ability, emotional job demands, and recognition by the management. Discrimination by the 9-predictor model was significant (AUC...... population. Implications for rehabilitation Long-term sickness absence risk predictions would enable healthcare providers to refer high-risk employees to rehabilitation programs aimed at preventing or reducing work disability. A prediction model based on health survey variables discriminates between...... employees at high and low risk of long-term sickness absence, but discrimination was not practically useful. Health survey variables provide insufficient information to determine long-term sickness absence risk profiles. There is a need for new variables, based on the knowledge and experience...

  3. Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings

    Science.gov (United States)

    Montoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; Kaminsky, Leonard A.

    2017-01-01

    This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a…

  4. Using Modeling and Simulation to Predict Operator Performance and Automation-Induced Complacency With Robotic Automation: A Case Study and Empirical Validation.

    Science.gov (United States)

    Wickens, Christopher D; Sebok, Angelia; Li, Huiyang; Sarter, Nadine; Gacy, Andrew M

    2015-09-01

    The aim of this study was to develop and validate a computational model of the automation complacency effect, as operators work on a robotic arm task, supported by three different degrees of automation. Some computational models of complacency in human-automation interaction exist, but those are formed and validated within the context of fairly simplified monitoring failures. This research extends model validation to a much more complex task, so that system designers can establish, without need for human-in-the-loop (HITL) experimentation, merits and shortcomings of different automation degrees. We developed a realistic simulation of a space-based robotic arm task that could be carried out with three different levels of trajectory visualization and execution automation support. Using this simulation, we performed HITL testing. Complacency was induced via several trials of correctly performing automation and then was assessed on trials when automation failed. Following a cognitive task analysis of the robotic arm operation, we developed a multicomponent model of the robotic operator and his or her reliance on automation, based in part on visual scanning. The comparison of model predictions with empirical results revealed that the model accurately predicted routine performance and predicted the responses to these failures after complacency developed. However, the scanning models do not account for the entire attention allocation effects of complacency. Complacency modeling can provide a useful tool for predicting the effects of different types of imperfect automation. The results from this research suggest that focus should be given to supporting situation awareness in automation development. © 2015, Human Factors and Ergonomics Society.

  5. Validation of a new mortality risk prediction model for people 65 years and older in northwest Russia: The Crystal risk score.

    Science.gov (United States)

    Turusheva, Anna; Frolova, Elena; Bert, Vaes; Hegendoerfer, Eralda; Degryse, Jean-Marie

    2017-07-01

    Prediction models help to make decisions about further management in clinical practice. This study aims to develop a mortality risk score based on previously identified risk predictors and to perform internal and external validations. In a population-based prospective cohort study of 611 community-dwelling individuals aged 65+ in St. Petersburg (Russia), all-cause mortality risks over 2.5 years follow-up were determined based on the results obtained from anthropometry, medical history, physical performance tests, spirometry and laboratory tests. C-statistic, risk reclassification analysis, integrated discrimination improvement analysis, decision curves analysis, internal validation and external validation were performed. Older adults were at higher risk for mortality [HR (95%CI)=4.54 (3.73-5.52)] when two or more of the following components were present: poor physical performance, low muscle mass, poor lung function, and anemia. If anemia was combined with high C-reactive protein (CRP) and high B-type natriuretic peptide (BNP) was added the HR (95%CI) was slightly higher (5.81 (4.73-7.14)) even after adjusting for age, sex and comorbidities. Our models were validated in an external population of adults 80+. The extended model had a better predictive capacity for cardiovascular mortality [HR (95%CI)=5.05 (2.23-11.44)] compared to the baseline model [HR (95%CI)=2.17 (1.18-4.00)] in the external population. We developed and validated a new risk prediction score that may be used to identify older adults at higher risk for mortality in Russia. Additional studies need to determine which targeted interventions improve the outcomes of these at-risk individuals. Copyright © 2017 Elsevier B.V. All rights reserved.

  6. External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom.

    Science.gov (United States)

    Harrison, David A; Griggs, Kathryn A; Prabhu, Gita; Gomes, Manuel; Lecky, Fiona E; Hutchinson, Peter J A; Menon, David K; Rowan, Kathryn M

    2015-10-01

    This study validates risk prediction models for acute traumatic brain injury (TBI) in critical care units in the United Kingdom and recalibrates the models to this population. The Risk Adjustment In Neurocritical care (RAIN) Study was a prospective, observational cohort study in 67 adult critical care units. Adult patients admitted to critical care following acute TBI with a last pre-sedation Glasgow Coma Scale score of less than 15 were recruited. The primary outcomes were mortality and unfavorable outcome (death or severe disability, assessed using the Extended Glasgow Outcome Scale) at six months following TBI. Of 3626 critical care unit admissions, 2975 were analyzed. Following imputation of missing outcomes, mortality at six months was 25.7% and unfavorable outcome 57.4%. Ten risk prediction models were validated from Hukkelhoven and colleagues, the Medical Research Council (MRC) Corticosteroid Randomisation After Significant Head Injury (CRASH) Trial Collaborators, and the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) group. The model with the best discrimination was the IMPACT "Lab" model (C index, 0.779 for mortality and 0.713 for unfavorable outcome). This model was well calibrated for mortality at six months but substantially under-predicted the risk of unfavorable outcome. Recalibration of the models resulted in small improvements in discrimination and excellent calibration for all models. The risk prediction models demonstrated sufficient statistical performance to support their use in research and audit but fell below the level required to guide individual patient decision-making. The published models for unfavorable outcome at six months had poor calibration in the UK critical care setting and the models recalibrated to this setting should be used in future research.

  7. Validation of a model for predicting smear-positive active pulmonary tuberculosis in patients with initial acid-fast bacilli smear-negative sputum

    Energy Technology Data Exchange (ETDEWEB)

    Yeh, Jun-Jun [Department of Chest Medicine, Section of Thoracic Imaging, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City (China); Chia Nan University of Pharmacy and Science, Tainan (China); Meiho University, Pingtung (China); Pingtung Christian Hospital, Pingtung (China); Heng Chun Christian Hospital, Pingtung (China)

    2018-01-15

    The objective of this study was to develop a predictive model for final smear-positive (SP) active pulmonary tuberculosis (aPTB) in patients with initial negative acid fast bacilli (AFB) sputum smears (iSN-SP-aPTB) based on high-resolution computed tomography (HRCT). Eighty (126, 21) patients of iSN-SP-aPTB and 402 (459, 876) patients of non-initial positive acid fast bacilli (non-iSP) pulmonary disease without iSN-SP-aPTB were included in a derivation (validation, prospective) cohort. HRCT characteristics were analysed, and multivariable regression and receiver operating characteristic (ROC) curve analysis was performed to develop a score predictive of iSN-SP-aPTB. The derivation cohort showed clusters of nodules/mass of the right upper lobe or left upper lobe were independent predictors of iSN-SP-aPTB, while bronchiectasis in the right middle lobe or left lingual lobe were negatively associated with iSN-SP-aPTB. A predictive score for iSN-SP-aPTB based on these findings was tested in the validation and prospective cohorts. With an ideal cut-off score = 1, the sensitivity, specificity, positive predictive value, and negative predictive value of the prediction model were 87.5% (90%, 90.5%), 99% (97.1%, 98.4%), 94.6% (81.3%, 57.5%), and 97.6% (97%, 99.8%) in the derivation (validation, prospective) cohorts, respectively. The model may help identify iSN-SP-aPTB among patients with non-iSP pulmonary diseases. circle Smear-positive active pulmonary tuberculosis that is initial smear-negative (iSN-SP-aPTB) is infectious. (orig.)

  8. The predictive validity of safety climate.

    Science.gov (United States)

    Johnson, Stephen E

    2007-01-01

    Safety professionals have increasingly turned their attention to social science for insight into the causation of industrial accidents. One social construct, safety climate, has been examined by several researchers [Cooper, M. D., & Phillips, R. A. (2004). Exploratory analysis of the safety climate and safety behavior relationship. Journal of Safety Research, 35(5), 497-512; Gillen, M., Baltz, D., Gassel, M., Kirsch, L., & Vacarro, D. (2002). Perceived safety climate, job Demands, and coworker support among union and nonunion injured construction workers. Journal of Safety Research, 33(1), 33-51; Neal, A., & Griffin, M. A. (2002). Safety climate and safety behaviour. Australian Journal of Management, 27, 66-76; Zohar, D. (2000). A group-level model of safety climate: Testing the effect of group climate on microaccidents in manufacturing jobs. Journal of Applied Psychology, 85(4), 587-596; Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group-level climates. Journal of Applied Psychology, 90(4), 616-628] who have documented its importance as a factor explaining the variation of safety-related outcomes (e.g., behavior, accidents). Researchers have developed instruments for measuring safety climate and have established some degree of psychometric reliability and validity. The problem, however, is that predictive validity has not been firmly established, which reduces the credibility of safety climate as a meaningful social construct. The research described in this article addresses this problem and provides additional support for safety climate as a viable construct and as a predictive indicator of safety-related outcomes. This study used 292 employees at three locations of a heavy manufacturing organization to complete the 16 item Zohar Safety Climate Questionnaire (ZSCQ) [Zohar, D., & Luria, G. (2005). A multilevel model of safety climate: Cross-level relationships between organization and group

  9. Validation of a Numerical Model for the Prediction of the Annoyance Condition at the Operator Station of Construction Machines

    Directory of Open Access Journals (Sweden)

    Eleonora Carletti

    2016-11-01

    Full Text Available It is well-known that the reduction of noise levels is not strictly linked to the reduction of noise annoyance. Even earthmoving machine manufacturers are facing the problem of customer complaints concerning the noise quality of their machines with increasing frequency. Unfortunately, all the studies geared to the understanding of the relationship between multidimensional characteristics of noise signals and the auditory perception of annoyance require repeated sessions of jury listening tests, which are time-consuming. In this respect, an annoyance prediction model was developed for compact loaders to assess the annoyance sensation perceived by operators at their workplaces without repeating the full sound quality assessment but using objective parameters only. This paper aims at verifying the feasibility of the developed annoyance prediction model when applied to other kinds of earthmoving machines. For this purpose, an experimental investigation was performed on five earthmoving machines, different in type, dimension, and engine mechanical power, and the annoyance predicted by the numerical model was compared to the annoyance given by subjective listening tests. The results were evaluated by means of the squared value of the correlation coefficient, R2, and they confirm the possible applicability of the model to other kinds of machines.

  10. Development and validation of a combined phased acoustical radiosity and image source model for predicting sound fields in rooms.

    Science.gov (United States)

    Marbjerg, Gerd; Brunskog, Jonas; Jeong, Cheol-Ho; Nilsson, Erling

    2015-09-01

    A model, combining acoustical radiosity and the image source method, including phase shifts on reflection, has been developed. The model is denoted Phased Acoustical Radiosity and Image Source Method (PARISM), and it has been developed in order to be able to model both specular and diffuse reflections with complex-valued and angle-dependent boundary conditions. This paper mainly describes the combination of the two models and the implementation of the angle-dependent boundary conditions. It furthermore describes how a pressure impulse response is obtained from the energy-based acoustical radiosity by regarding the model as being stochastic. Three methods of implementation are proposed and investigated, and finally, recommendations are made for their use. Validation of the image source method is done by comparison with finite element simulations of a rectangular room with a porous absorber ceiling. Results from the full model are compared with results from other simulation tools and with measurements. The comparisons of the full model are done for real-valued and angle-independent surface properties. The proposed model agrees well with both the measured results and the alternative theories, and furthermore shows a more realistic spatial variation than energy-based methods due to the fact that interference is considered.

  11. Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team.

    Science.gov (United States)

    Harrison, David A; Patel, Krishna; Nixon, Edel; Soar, Jasmeet; Smith, Gary B; Gwinnutt, Carl; Nolan, Jerry P; Rowan, Kathryn M

    2014-08-01

    The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. Risk models for two outcomes-return of spontaneous circulation (ROSC) for greater than 20min and survival to hospital discharge-were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC>20min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC>20min (c index 0.81 versus 0.72). Validated risk models for ROSC>20min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  12. Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team☆

    Science.gov (United States)

    Harrison, David A.; Patel, Krishna; Nixon, Edel; Soar, Jasmeet; Smith, Gary B.; Gwinnutt, Carl; Nolan, Jerry P.; Rowan, Kathryn M.

    2014-01-01

    Aim The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. Methods Risk models for two outcomes—return of spontaneous circulation (ROSC) for greater than 20 min and survival to hospital discharge—were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. Results 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC > 20 min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC > 20 min (c index 0.81 versus 0.72). Conclusions Validated risk models for ROSC > 20 min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. PMID:24830872

  13. Watershed Modeling with ArcSWAT and SUFI2 In Cisadane Catchment Area: Calibration and Validation of River Flow Prediction

    Directory of Open Access Journals (Sweden)

    Iwan Ridwansyah

    2014-04-01

    Full Text Available Increasing of natural resources utilization as a result of population growth and economic development has caused severe damage on the watershed. The impacts of natural disasters such as floods, landslides and droughts become more frequent. Cisadane Catchment Area is one of 108 priority watershed in Indonesia. SWAT is currently applied world wide and considered as a versatile model that can be used to integrate multiple environmental processes, which support more effective watershed management and the development of better informed policy decision. The objective of this study is to examine the applicability of SWAT model for modeling mountainous catchments, focusing on Cisadane catchment Area in west Java Province, Indonesia. The SWAT model simulation was done for the periods of 2005 – 2010 while it used landuse information in 2009. Methods of Sequential Uncertainty Fitting ver. 2 (SUFI2 and combine with manual calibration were used in this study to calibrate a rainfall-runoff. The Calibration is done on 2007 and the validation on 2009, the R2 and Nash Sutchliffe Efficiency (NSE of the calibration were 0.71 and 0.72 respectively and the validation are 0.708 and 0.7 respectively. The monthly average of surface runoff and total water yield from the simulation were 27.7 mm and 2718.4 mm respectively. This study showed SWAT model can be a potential monitoring tool especially for watersheds in Cisadane Catchment Area or in the tropical regions. The model can be used for another purpose, especially in watershed management.

  14. Validation of a zero-dimensional model for prediction of NOx and engine performance for electronically controlled marine two-stroke diesel engines

    DEFF Research Database (Denmark)

    Scappin, Fabio; Stefansson, Sigurður H.; Haglind, Fredrik

    2012-01-01

    The aim of this paper is to derive a methodology suitable for energy system analysis for predicting the performance and NOx emissions of marine low speed diesel engines. The paper describes a zero-dimensional model, evaluating the engine performance by means of an energy balance and a two zone...... experimental data from two MAN B&W engines; one case being data subject to engine parameter changes corresponding to simulating an electronically controlled engine; the second case providing data covering almost all model input and output parameters. The first case of validation suggests that the model can...

  15. Modeling and validation of a mechanistic tool (MEFISTO) for the prediction of critical power in BWR fuel assemblies

    International Nuclear Information System (INIS)

    Adamsson, Carl; Le Corre, Jean-Marie

    2011-01-01

    Highlights: → The MEFISTO code efficiently and accurately predicts the dryout event in a BWR fuel bundle, using a mechanistic model. → A hybrid approach between a fast and robust sub-channel analysis and a three-field two-phase analysis is adopted. → MEFISTO modeling approach, calibration, CPU usage, sensitivity, trend analysis and performance evaluation are presented. → The calibration parameters and process were carefully selected to preserve the mechanistic nature of the code. → The code dryout prediction performance is near the level of fuel-specific empirical dryout correlations. - Abstract: Westinghouse is currently developing the MEFISTO code with the main goal to achieve fast, robust, practical and reliable prediction of steady-state dryout Critical Power in Boiling Water Reactor (BWR) fuel bundle based on a mechanistic approach. A computationally efficient simulation scheme was used to achieve this goal, where the code resolves all relevant field (drop, steam and multi-film) mass balance equations, within the annular flow region, at the sub-channel level while relying on a fast and robust two-phase (liquid/steam) sub-channel solution to provide the cross-flow information. The MEFISTO code can hence provide highly detailed solution of the multi-film flow in BWR fuel bundle while enhancing flexibility and reducing the computer time by an order of magnitude as compared to a standard three-field sub-channel analysis approach. Models for the numerical computation of the one-dimensional field flowrate distributions in an open channel (e.g. a sub-channel), including the numerical treatment of field cross-flows, part-length rods, spacers grids and post-dryout conditions are presented in this paper. The MEFISTO code is then applied to dryout prediction in BWR fuel bundle using VIPRE-W as a fast and robust two-phase sub-channel driver code. The dryout power is numerically predicted by iterating on the bundle power so that the minimum film flowrate in the

  16. Prospective validation of a predictive model that identifies homeless people at risk of re-presentation to the emergency department.

    Science.gov (United States)

    Moore, Gaye; Hepworth, Graham; Weiland, Tracey; Manias, Elizabeth; Gerdtz, Marie Frances; Kelaher, Margaret; Dunt, David

    2012-02-01

    To prospectively evaluate the accuracy of a predictive model to identify homeless people at risk of representation to an emergency department. A prospective cohort analysis utilised one month of data from a Principal Referral Hospital in Melbourne, Australia. All visits involving people classified as homeless were included, excluding those who died. Homelessness was defined as living on the streets, in crisis accommodation, in boarding houses or residing in unstable housing. Rates of re-presentation, defined as the total number of visits to the same emergency department within 28 days of discharge from hospital, were measured. Performance of the risk screening tool was assessed by calculating sensitivity, specificity, positive and negative predictive values and likelihood ratios. Over the study period (April 1, 2009 to April 30, 2009), 3298 presentations from 2888 individuals were recorded. The homeless population accounted for 10% (n=327) of all visits and 7% (n=211) of all patients. A total of 90 (43%) homeless people re-presented to the emergency department. The predictive model included nine variables and achieved 98% (CI, 0.92-0.99) sensitivity and 66% (CI, 0.57-0.74) specificity. The positive predictive value was 68% and the negative predictive value was 98%. The positive likelihood ratio 2.9 (CI, 2.2-3.7) and the negative likelihood ratio was 0.03 (CI, 0.01-0.13). The high emergency department re-presentation rate for people who were homeless identifies unresolved psychosocial health needs. The emergency department remains a vital access point for homeless people, particularly after hours. The risk screening tool is key to identify medical and social aspects of a homeless patient's presentation to assist early identification and referral. Copyright © 2012 College of Emergency Nursing Australasia Ltd. Published by Elsevier Ltd. All rights reserved.

  17. Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

    Science.gov (United States)

    Lee, Christine K; Hofer, Ira; Gabel, Eilon; Baldi, Pierre; Cannesson, Maxime

    2018-04-17

    The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

  18. Clinical validation of the LKB model and parameter sets for predicting radiation-induced pneumonitis from breast cancer radiotherapy

    International Nuclear Information System (INIS)

    Tsougos, Ioannis; Mavroidis, Panayiotis; Theodorou, Kyriaki; Rajala, J; Pitkaenen, M A; Holli, K; Ojala, A T; Hyoedynmaa, S; Jaervenpaeae, Ritva; Lind, Bengt K; Kappas, Constantin

    2006-01-01

    The choice of the appropriate model and parameter set in determining the relation between the incidence of radiation pneumonitis and dose distribution in the lung is of great importance, especially in the case of breast radiotherapy where the observed incidence is fairly low. From our previous study based on 150 breast cancer patients, where the fits of dose-volume models to clinical data were estimated (Tsougos et al 2005 Evaluation of dose-response models and parameters predicting radiation induced pneumonitis using clinical data from breast cancer radiotherapy Phys. Med. Biol. 50 3535-54), one could get the impression that the relative seriality is significantly better than the LKB NTCP model. However, the estimation of the different NTCP models was based on their goodness-of-fit on clinical data, using various sets of published parameters from other groups, and this fact may provisionally justify the results. Hence, we sought to investigate further the LKB model, by applying different published parameter sets for the very same group of patients, in order to be able to compare the results. It was shown that, depending on the parameter set applied, the LKB model is able to predict the incidence of radiation pneumonitis with acceptable accuracy, especially when implemented on a sub-group of patients (120) receiving D-bar-bar vertical bar EUD higher than 8 Gy. In conclusion, the goodness-of-fit of a certain radiobiological model on a given clinical case is closely related to the selection of the proper scoring criteria and parameter set as well as to the compatibility of the clinical case from which the data were derived. (letter to the editor)

  19. Model description and evaluation of model performance, scenario S. Multiple pathways assessment of the IAEA/CEC co-ordinated research programme on validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    Suolanen, V.

    1996-12-01

    A modelling approach was used to predict doses from a large area deposition of 137 Cs over southern and central Finland. The assumed deposition profile and quantity were both similar to those resulting from the Chernobyl accident. In the study, doses via terrestrial and aquatic environments have been analyzed. Additionally, the intermediate results of the study, such as concentrations in various foodstuffs and the resulting body burdents, were presented. The contributions of ingestion, inhalation and external doses to the total dose were estimated in the study. The considered deposition scenario formed a modelling exercise in the IAEA coordinated research programme on Validation of Environmental Model Predictions, the VAMP project. (21 refs.)

  20. Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes.

    Science.gov (United States)

    Kononen, Douglas W; Flannagan, Carol A C; Wang, Stewart C

    2011-01-01

    A multivariate logistic regression model, based upon National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data for calendar years 1999-2008, was developed to predict the probability that a crash-involved vehicle will contain one or more occupants with serious or incapacitating injuries. These vehicles were defined as containing at least one occupant coded with an Injury Severity Score (ISS) of greater than or equal to 15, in planar, non-rollover crash events involving Model Year 2000 and newer cars, light trucks, and vans. The target injury outcome measure was developed by the Centers for Disease Control and Prevention (CDC)-led National Expert Panel on Field Triage in their recent revision of the Field Triage Decision Scheme (American College of Surgeons, 2006). The parameters to be used for crash injury prediction were subsequently specified by the National Expert Panel. Model input parameters included: crash direction (front, left, right, and rear), change in velocity (delta-V), multiple vs. single impacts, belt use, presence of at least one older occupant (≥ 55 years old), presence of at least one female in the vehicle, and vehicle type (car, pickup truck, van, and sport utility). The model was developed using predictor variables that may be readily available, post-crash, from OnStar-like telematics systems. Model sensitivity and specificity were 40% and 98%, respectively, using a probability cutpoint of 0.20. The area under the receiver operator characteristic (ROC) curve for the final model was 0.84. Delta-V (mph), seat belt use and crash direction were the most important predictors of serious injury. Due to the complexity of factors associated with rollover-related injuries, a separate screening algorithm is needed to model injuries associated with this crash mode. Copyright © 2010 Elsevier Ltd. All rights reserved.

  1. Validating predictions made by a thermo-mechanical model of melt segregation in sub-volcanic systems

    Science.gov (United States)

    Roele, Katarina; Jackson, Matthew; Morgan, Joanna

    2014-05-01

    A quantitative understanding of the spatial and temporal evolution of melt distribution in the crust is crucial in providing insights into the development of sub-volcanic crustal stratigraphy and composition. This work aims to relate numerical models that describe the base of volcanic systems with geophysical observations. Recent modelling has shown that the repetitive emplacement of mantle-derived basaltic sills, at the base of the lower crust, acts as a heat source for anatectic melt generation, buoyancy-driven melt segregation and mobilisation. These processes form the lowermost architecture of complex sub-volcanic networks as upward migrating melt produces high melt fraction layers. These 'porosity waves' are separated by zones with high compaction rates and have distinctive polybaric chemical signatures that suggest mixed crust and mantle origins. A thermo-mechanical model produced by Solano et al in 2012 has been used to predict the temperatures and melt fractions of successive high porosity layers within the crust. This model was used as it accounts for the dynamic evolution of melt during segregation and migration through the crust; a significant process that has been neglected in previous models. The results were used to input starting compositions for each of the layers into the rhyolite-MELTS thermodynamic simulation. MELTS then determined the approximate bulk composition of the layers once they had cooled and solidified. The mean seismic wave velocities of the polymineralic layers were then calculated using the relevant Voight-Reuss-Hill mixture rules, whilst accounting for the pressure and temperature dependence of seismic wave velocity. The predicted results were then compared with real examples of reflectivity for areas including the UK, where lower crustal layering is observed. A comparison between the impedance contrasts at compositional boundaries is presented as it confirms the extent to which modelling is able to make predictions that are

  2. Validation of mathematical models for the prediction of organs-at-risk dosimetric metrics in high-dose-rate gynecologic interstitial brachytherapy

    Energy Technology Data Exchange (ETDEWEB)

    Damato, Antonio L.; Viswanathan, Akila N.; Cormack, Robert A. [Dana-Farber Cancer Institute and Brigham and Women' s Hospital, Boston, Massachusetts 02115 (United States)

    2013-10-15

    Purpose: Given the complicated nature of an interstitial gynecologic brachytherapy treatment plan, the use of a quantitative tool to evaluate the quality of the achieved metrics compared to clinical practice would be advantageous. For this purpose, predictive mathematical models to predict the D{sub 2cc} of rectum and bladder in interstitial gynecologic brachytherapy are discussed and validated.Methods: Previous plans were used to establish the relationship between D2cc and the overlapping volume of the organ at risk with the targeted area (C0) or a 1-cm expansion of the target area (C1). Three mathematical models were evaluated: D{sub 2cc}=α*C{sub 1}+β (LIN); D{sub 2cc}=α– exp(–β*C{sub 0}) (EXP); and a mixed approach (MIX), where both C{sub 0} and C{sub 1} were inputs of the model. The parameters of the models were optimized on a training set of patient data, and the predictive error of each model (predicted D{sub 2cc}− real D{sub 2cc}) was calculated on a validation set of patient data. The data of 20 patients were used to perform a K-fold cross validation analysis, with K = 2, 4, 6, 8, 10, and 20.Results: MIX was associated with the smallest mean prediction error <6.4% for an 18-patient training set; LIN had an error <8.5%; EXP had an error <8.3%. Best case scenario analysis shows that an error ≤5% can be achieved for a ten-patient training set with MIX, an error ≤7.4% for LIN, and an error ≤6.9% for EXP. The error decreases with the increase in training set size, with the most marked decrease observed for MIX.Conclusions: The MIX model can predict the D{sub 2cc} of the organs at risk with an error lower than 5% with a training set of ten patients or greater. The model can be used in the development of quality assurance tools to identify treatment plans with suboptimal sparing of the organs at risk. It can also be used to improve preplanning and in the development of real-time intraoperative planning tools.

  3. Attempted development and cross-validation of predictive models of individual-level and organizational-level turnover of nuclear power operators

    International Nuclear Information System (INIS)

    Vasa-Sideris, S.J.

    1989-01-01

    Nuclear power accounts for 209% of the electric power generated in the U.S. by 107 nuclear plants which employ over 8,700 operators. Operator turnover is significant to utilities from the economic point of view since it costs almost three hundred thousand dollars to train and qualify one operator, and because turnover affects plant operability and therefore plant safety. The study purpose was to develop and cross-validate individual-level and organizational-level models of turnover of nuclear power plant operators. Data were obtained by questionnaires and from published data for 1983 and 1984 on a number of individual, organizational, and environmental predictors. Plants had been in operation for two or more years. Questionnaires were returned by 29 out of 50 plants on over 1600 operators. The objectives were to examine the reliability of the turnover criterion, to determine the classification accuracy of the multivariate predictive models and of categories of predictors (individual, organizational, and environmental) and to determine if a homology existed between the individual-level and organizational-level models. The method was to examine the shrinkage that occurred between foldback design (in which the predictive models were reapplied to the data used to develop them) and cross-validation. Results did not support the hypothesis objectives. Turnover data were accurate but not stable between the two years. No significant differences were detected between the low and high turnover groups at the organization or individual level in cross-validation. Lack of stability in the criterion, restriction of range, and small sample size at the organizational level were serious limitations of this study. The results did support the methods. Considerable shrinkage occurred between foldback and cross-validation of the models

  4. Scale Model Acoustic Test Validation of IOP-SS Water Prediction using Loci-STREAM-VoF

    Science.gov (United States)

    Nielsen, Tanner; West, Jeff

    2015-01-01

    The Scale Model Acoustic Test (SMAT) is a 5% scale test of the Space Launch System (SLS), which is currently being designed at Marshall Space Flight Center (MSFC). SMAT consists of a 5% scale representation of the ignition overpressure sound-suppression system (IOP-SS) that is being tested to quantify the water flow and induced air entrainment in and around the mobile launcher exhaust hole. This data will be compared with computational fluid dynamics (CFD) simulations using the newly developed Loci-STREAM Volume of Fluid (VoF) methods. Compressible and incompressible VoF methods have been formulated, and are currently being used to simulate the water flow of SMAT IOP-SS. The test data will be used to qualitatively and quantitatively assess and validate the VoF methods.

  5. Prediction of the hardness profile of an AISI 4340 steel cylinder heat-treated by laser - 3D and artificial neural networks modelling and experimental validation

    Energy Technology Data Exchange (ETDEWEB)

    Hadhri, Mahdi; Ouafi, Abderazzak El; Barka, Noureddine [University of Quebec, Rimouski (Canada)

    2017-02-15

    This paper presents a comprehensive approach developed to design an effective prediction model for hardness profile in laser surface transformation hardening process. Based on finite element method and Artificial neural networks, the proposed approach is built progressively by (i) examining the laser hardening parameters and conditions known to have an influence on the hardened surface attributes through a structured experimental investigation, (ii) investigating the laser hardening parameters effects on the hardness profile through extensive 3D modeling and simulation efforts and (ii) integrating the hardening process parameters via neural network model for hardness profile prediction. The experimental validation conducted on AISI4340 steel using a commercial 3 kW Nd:Yag laser, confirm the feasibility and efficiency of the proposed approach leading to an accurate and reliable hardness profile prediction model. With a maximum relative error of about 10 % under various practical conditions, the predictive model can be considered as effective especially in the case of a relatively complex system such as laser surface transformation hardening process.

  6. Prediction of the hardness profile of an AISI 4340 steel cylinder heat-treated by laser - 3D and artificial neural networks modelling and experimental validation

    International Nuclear Information System (INIS)

    Hadhri, Mahdi; Ouafi, Abderazzak El; Barka, Noureddine

    2017-01-01

    This paper presents a comprehensive approach developed to design an effective prediction model for hardness profile in laser surface transformation hardening process. Based on finite element method and Artificial neural networks, the proposed approach is built progressively by (i) examining the laser hardening parameters and conditions known to have an influence on the hardened surface attributes through a structured experimental investigation, (ii) investigating the laser hardening parameters effects on the hardness profile through extensive 3D modeling and simulation efforts and (ii) integrating the hardening process parameters via neural network model for hardness profile prediction. The experimental validation conducted on AISI4340 steel using a commercial 3 kW Nd:Yag laser, confirm the feasibility and efficiency of the proposed approach leading to an accurate and reliable hardness profile prediction model. With a maximum relative error of about 10 % under various practical conditions, the predictive model can be considered as effective especially in the case of a relatively complex system such as laser surface transformation hardening process

  7. Development and validation of a new virtual source model for portal image prediction and treatment quality control

    International Nuclear Information System (INIS)

    Chabert, Isabelle

    2015-01-01

    Intensity-Modulated Radiation Therapy (IMRT), require extensive verification procedures to ensure the correct dose delivery. Electronic Portal Imaging Devices (EPIDs) are widely used for quality assurance in radiotherapy, and also for dosimetric verifications. For this latter application, the images obtained during the treatment session can be compared to a pre-calculated reference image in order to highlight dose delivery errors. The quality control performance depends (1) on the accuracy of the pre-calculated reference image (2) on the ability of the tool used to compare images to detect errors. These two key points were studied during this PhD work. We chose to use a Monte Carlo (MC)-based method developed in the laboratory and based on the DPGLM (Dirichlet process generalized linear model) de-noising technique to predict high-resolution reference images. A model of the studied linear accelerator (linac Synergy, Elekta, Crawley, UK) was first developed using the PENELOPE MC codes, and then commissioned using measurements acquired in the Hopital Nord of Marseille. A 71 Go phase space file (PSF) stored under the flattening filter was then analyzed to build a new kind of virtual source model based on correlated histograms (200 Mo). This new and compact VSM is as much accurate as the PSF to calculate dose distributions in water if histogram sampling is based on adaptive method. The associated EPID modelling in PENELOPE suggests that hypothesis about linac primary source were too simple and should be reconsidered. The use of the VSM to predict high-resolution portal images however led to excellent results. The VSM associated to the linac and EPID MC models were used to detect errors in IMRT treatment plans. A preliminary study was conducted introducing on purpose treatment errors in portal image calculations (primary source parameters, phantom position and morphology changes). The γ-index commonly used in clinical routine appears to be less effective than the

  8. BIOMOVS: an international model validation study

    International Nuclear Information System (INIS)

    Haegg, C.; Johansson, G.

    1988-01-01

    BIOMOVS (BIOspheric MOdel Validation Study) is an international study where models used for describing the distribution of radioactive and nonradioactive trace substances in terrestrial and aquatic environments are compared and tested. The main objectives of the study are to compare and test the accuracy of predictions between such models, explain differences in these predictions, recommend priorities for future research concerning the improvement of the accuracy of model predictions and act as a forum for the exchange of ideas, experience and information. (author)

  9. BIOMOVS: An international model validation study

    International Nuclear Information System (INIS)

    Haegg, C.; Johansson, G.

    1987-01-01

    BIOMOVS (BIOspheric MOdel Validation Study) is an international study where models used for describing the distribution of radioactive and nonradioactive trace substances in terrestrial and aquatic environments are compared and tested. The main objectives of the study are to compare and test the accuracy of predictions between such models, explain differences in these predictions, recommend priorities for future research concerning the improvement of the accuracy of model predictions and act as a forum for the exchange of ideas, experience and information. (orig.)

  10. Predictability analysis and validation of a low-dimensional model - an application to the dynamics of cereal crops observed from satellite

    Science.gov (United States)

    Mangiarotti, Sylvain; Drapeau, Laurent

    2013-04-01

    The global modeling approach aims to obtain parsimonious models of observed dynamics from few or single time series (Letellier et al. 2009). Specific algorithms were developed and validated for this purpose (Mangiarotti et al. 2012a). This approach was applied to the dynamics of cereal crops in semi-arid region using the vegetation index derived from satellite data as a proxy of the dynamics. A low-dimensional autonomous model could be obtained. The corresponding attractor is characteristic of weakly dissipative chaos and exhibits a toroidal-like structure. At present, only few theoretical cases of such chaos are known, and none was obtained from real world observations. Under smooth conditions, a robust validation of three-dimensional chaotic models can be usually performed based on the topological approach (Gilmore 1998). Such approach becomes more difficult for weakly dissipative systems, and almost impossible under noisy observational conditions. For this reason, another validation approach is developed which consists in comparing the forecasting skill of the model to other forecasts for which no dynamical model is required. A data assimilation process is associated to the model to estimate the model's skill; several schemes are tested (simple re-initialization, Extended and Ensemble Kalman Filters and Back and Forth Nudging). Forecasts without model are performed based on the search of analogous states in the phase space (Mangiarotti et al. 2012b). The comparison reveals the quality of the model's forecasts at short to moderate horizons and contributes to validate the model. These results suggest that the dynamics of cereal crops can be reasonably approximated by low-dimensional chaotic models, and also bring out powerful arguments for chaos. Chaotic models have often been used as benchmark to test data assimilation schemes; the present work shows that such tests may not only have a theoretical interest, but also almost direct applicative potential. Moreover

  11. Performance of an easy-to-use prediction model for renal patient survival: an external validation study using data from the ERA-EDTA Registry.

    Science.gov (United States)

    Hemke, Aline C; Heemskerk, Martin B A; van Diepen, Merel; Kramer, Anneke; de Meester, Johan; Heaf, James G; Abad Diez, José Maria; Torres Guinea, Marta; Finne, Patrik; Brunet, Philippe; Vikse, Bjørn E; Caskey, Fergus J; Traynor, Jamie P; Massy, Ziad A; Couchoud, Cécile; Groothoff, Jaap W; Nordio, Maurizio; Jager, Kitty J; Dekker, Friedo W; Hoitsma, Andries J

    2018-01-16

    An easy-to-use prediction model for long-term renal patient survival based on only four predictors [age, primary renal disease, sex and therapy at 90 days after the start of renal replacement therapy (RRT)] has been developed in The Netherlands. To assess the usability of this model for use in Europe, we externally validated the model in 10 European countries. Data from the European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry were used. Ten countries that reported individual patient data to the registry on patients starting RRT in the period 1995-2005 were included. Patients prediction model was evaluated for the 10- (primary endpoint), 5- and 3-year survival predictions by assessing the calibration and discrimination outcomes. We used a data set of 136 304 patients from 10 countries. The calibration in the large and calibration plots for 10 deciles of predicted survival probabilities showed average differences of 1.5, 3.2 and 3.4% in observed versus predicted 10-, 5- and 3-year survival, with some small variation on the country level. The concordance index, indicating the discriminatory power of the model, was 0.71 in the complete ERA-EDTA Registry cohort and varied according to country level between 0.70 and 0.75. A prediction model for long-term renal patient survival developed in a single country, based on only four easily available variables, has a comparably adequate performance in a wide range of other European countries. © The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  12. External validation of Vascular Study Group of New England risk predictive model of mortality after elective abdominal aorta aneurysm repair in the Vascular Quality Initiative and comparison against established models.

    Science.gov (United States)

    Eslami, Mohammad H; Rybin, Denis V; Doros, Gheorghe; Siracuse, Jeffrey J; Farber, Alik

    2018-01-01

    The purpose of this study is to externally validate a recently reported Vascular Study Group of New England (VSGNE) risk predictive model of postoperative mortality after elective abdominal aortic aneurysm (AAA) repair and to compare its predictive ability across different patients' risk categories and against the established risk predictive models using the Vascular Quality Initiative (VQI) AAA sample. The VQI AAA database (2010-2015) was queried for patients who underwent elective AAA repair. The VSGNE cases were excluded from the VQI sample. The external validation of a recently published VSGNE AAA risk predictive model, which includes only preoperative variables (age, gender, history of coronary artery disease, chronic obstructive pulmonary disease, cerebrovascular disease, creatinine levels, and aneurysm size) and planned type of repair, was performed using the VQI elective AAA repair sample. The predictive value of the model was assessed via the C-statistic. Hosmer-Lemeshow method was used to assess calibration and goodness of fit. This model was then compared with the Medicare, Vascular Governance Northwest model, and Glasgow Aneurysm Score for predicting mortality in VQI sample. The Vuong test was performed to compare the model fit between the models. Model discrimination was assessed in different risk group VQI quintiles. Data from 4431 cases from the VSGNE sample with the overall mortality rate of 1.4% was used to develop the model. The internally validated VSGNE model showed a very high discriminating ability in predicting mortality (C = 0.822) and good model fit (Hosmer-Lemeshow P = .309) among the VSGNE elective AAA repair sample. External validation on 16,989 VQI cases with an overall 0.9% mortality rate showed very robust predictive ability of mortality (C = 0.802). Vuong tests yielded a significant fit difference favoring the VSGNE over then Medicare model (C = 0.780), Vascular Governance Northwest (0.774), and Glasgow Aneurysm Score (0

  13. Validation of Monte Carlo predictions of LWR-PROTEUS safety parameters using an improved whole-reactor model

    Energy Technology Data Exchange (ETDEWEB)

    Plaschy, M. [Laboratory for Reactor Physics and Systems Behaviour, Paul Scherrer Institute, CH-5232 Villigen, PSI (Switzerland)], E-mail: michael.plaschy@eos.ch; Murphy, M.; Jatuff, F.; Perret, G.; Seiler, R. [Laboratory for Reactor Physics and Systems Behaviour, Paul Scherrer Institute, CH-5232 Villigen, PSI (Switzerland); Chawla, R. [Laboratory for Reactor Physics and Systems Behaviour, Paul Scherrer Institute, CH-5232 Villigen, PSI (Switzerland); Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, EPFL (Switzerland)

    2009-10-15

    The recent experimental programme conducted in the PROTEUS research reactor at the Paul Scherrer Institute (PSI) has concerned detailed investigations of advanced light water reactor (LWR) fuels. More than fifteen different configurations of the multi-zone critical facility have been studied, each of them requiring accurate estimation of operational safety parameters, in particular the critical driver loadings, shutdown rod worths and the effective delayed neutron fraction {beta}{sub eff}. The current paper presents a full-scale 3D Monte Carlo model for the facility, set up using the MCNPX code, which has been employed for calculation of the operational characteristics for seven different LWR-PROTEUS configurations. Thereby, a variety of nuclear data libraries (viz. ENDF/B6v2, ENDF/B6v8, JEF2.2, JEFF3.0, JEFF3.1, JENDL3.2, and JENDL3.3) have been used, and predictions of k{sub eff} and shutdown rod worths compared with experimental values. Even though certain library-specific trends have been observed, the k{sub eff} predictions are generally very satisfactory, viz. with discrepancies of <0.5% between calculation (C) and experiment (E). The results also confirm the consistent determination of reactivity variations, the C/E values for the shutdown (safety) rod worths being always within 5% of unity. In addition, the MCNP modelling of the multi-zone reactor has yielded interesting results for the delayed neutron fraction ({beta}{sub eff}) in the different configurations, a breakdown being made possible in each case in terms of delayed neutron group, fissioning nuclide, and reactor region.

  14. External validation of the GrazeIn model of pasture dry matter intake and milk yield prediction for cows managed at different calving dates and stocking rates

    International Nuclear Information System (INIS)

    Roca-Fernández, A.I.; González-Rodríguez, A.

    2017-01-01

    The aim was to evaluate the prediction accuracy of pasture dry matter intake (PDMI) and milk yield (MY) predicted by the GrazeIn model using a database representing 124 PDMI measurements at paddock level and 2232 MY measurements at cow level. External validation of the model was conducted using data collected from a trial carried out with Holstein-Friesian cows (n=72) while grazed 28 paddocks and were managed in a 2×2 factorial design by considering two calving dates (CD), with different number of days in milk (DIM), early (E, 29 DIM) vs. middle (M, 167 DIM), and two stocking rates (SR), medium (M, 3.9 cows ha-1) vs. high (H, 4.8 cows ha-1), under a rotational grazing system. Cows were randomly assigned to four grazing scenarios (EM, EH, MM and MH). The mean observed PDMI of the total database was 14.2 kg DM cow-1 day-1 while GrazeIn predicted a mean PDMI for the database of 13.8 kg DM cow-1 day-1. The mean bias was −0.4 kg DM cow-1 day-1. GrazeIn predicted PDMI for the total database with a relative prediction error (RPE) of 10.0% at paddock level. The mean observed MY of the database was 23.2 kg cow-1 day-1 while GrazeIn predicted a MY for the database of 23.1 kg cow-1 day-1. The mean bias was –0.1 kg cow-1 day-1. GrazeIn predicted MY for the total database with a mean RPE of 17.3% at cow level. For the scenarios investigated, GrazeIn predicted PDMI and MY with a low level of error which made it a suitable tool for decision support systems.

  15. External validation of the GrazeIn model of pasture dry matter intake and milk yield prediction for cows managed at different calving dates and stocking rates

    Energy Technology Data Exchange (ETDEWEB)

    Roca-Fernández, A.I.; González-Rodríguez, A.

    2017-07-01

    The aim was to evaluate the prediction accuracy of pasture dry matter intake (PDMI) and milk yield (MY) predicted by the GrazeIn model using a database representing 124 PDMI measurements at paddock level and 2232 MY measurements at cow level. External validation of the model was conducted using data collected from a trial carried out with Holstein-Friesian cows (n=72) while grazed 28 paddocks and were managed in a 2×2 factorial design by considering two calving dates (CD), with different number of days in milk (DIM), early (E, 29 DIM) vs. middle (M, 167 DIM), and two stocking rates (SR), medium (M, 3.9 cows ha-1) vs. high (H, 4.8 cows ha-1), under a rotational grazing system. Cows were randomly assigned to four grazing scenarios (EM, EH, MM and MH). The mean observed PDMI of the total database was 14.2 kg DM cow-1 day-1 while GrazeIn predicted a mean PDMI for the database of 13.8 kg DM cow-1 day-1. The mean bias was −0.4 kg DM cow-1 day-1. GrazeIn predicted PDMI for the total database with a relative prediction error (RPE) of 10.0% at paddock level. The mean observed MY of the database was 23.2 kg cow-1 day-1 while GrazeIn predicted a MY for the database of 23.1 kg cow-1 day-1. The mean bias was –0.1 kg cow-1 day-1. GrazeIn predicted MY for the total database with a mean RPE of 17.3% at cow level. For the scenarios investigated, GrazeIn predicted PDMI and MY with a low level of error which made it a suitable tool for decision support systems.

  16. Thermal niche for in situ seed germination by Mediterranean mountain streams: model prediction and validation for Rhamnus persicifolia seeds

    Science.gov (United States)

    Porceddu, Marco; Mattana, Efisio; Pritchard, Hugh W.; Bacchetta, Gianluigi

    2013-01-01

    Background and Aims Mediterranean mountain species face exacting ecological conditions of rainy, cold winters and arid, hot summers, which affect seed germination phenology. In this study, a soil heat sum model was used to predict field emergence of Rhamnus persicifolia, an endemic tree species living at the edge of mountain streams of central eastern Sardinia. Methods Seeds were incubated in the light at a range of temperatures (10–25 and 25/10 °C) after different periods (up to 3 months) of cold stratification at 5 °C. Base temperatures (Tb), and thermal times for 50 % germination (θ50) were calculated. Seeds were also buried in the soil in two natural populations (Rio Correboi and Rio Olai), both underneath and outside the tree canopy, and exhumed at regular intervals. Soil temperatures were recorded using data loggers and soil heat sum (°Cd) was calculated on the basis of the estimated Tb and soil temperatures. Key Results Cold stratification released physiological dormancy (PD), increasing final germination and widening the range of germination temperatures, indicative of a Type 2 non-deep PD. Tb was reduced from 10·5 °C for non-stratified seeds to 2·7 °C for seeds cold stratified for 3 months. The best thermal time model was obtained by fitting probit germination against log °Cd. θ50 was 2·6 log °Cd for untreated seeds and 2·17–2·19 log °Cd for stratified seeds. When θ50 values were integrated with soil heat sum estimates, field emergence was predicted from March to April and confirmed through field observations. Conclusions Tb and θ50 values facilitated model development of the thermal niche for in situ germination of R. persicifolia. These experimental approaches may be applied to model the natural regeneration patterns of other species growing on Mediterranean mountain waterways and of physiologically dormant species, with overwintering cold stratification requirement and spring germination. PMID:24201139

  17. Prediction of concentration and model validation - key issues in assessment of long term safety for radioactive waste disposal

    International Nuclear Information System (INIS)

    Xu, S.; Dverstorp, B.; Woerman, A.

    2008-01-01

    Post-closure safety assessments for nuclear waste repositories involve radioecological modelling for en,underground source term. In this paper we discuss critical aspects concerning process understanding and justification of simplified radioecological models used for such safety assessments. This study is part of the Swedish Radiation Protection Authority's (SSI) work on reviewing the Swedish Nuclear Fuel and Waste Management Co's (SKB) most recent safety assessment, SR-Can. One of the most challenging tasks in assessments of environmental doses and risk from an underground repository is to estimate radionuclide activity concentrations in various geologic strata in the future. For example, little is known about transport pathways through the quaternary deposits to the discharge points in surface waters and other recipients in the biosphere. Traditionally simplified compartmental models are used in safety assessment to describe the fate of radio-nuclides in surface environment. The possibility to test such models against more detailed process models and site specific data is of key importance for confidence in the safety assessment. As part of SSI's review of SR-Can, alternative modelling approaches were developed to explore the importance of transport process descriptions in the assessment models. The modelling results were compared with the Landscape Dose Factors (LDFs) derived by SKB in SR-Can. LDFs is a new methodology adapted by SKB in SR-Can. The LDFs are defined in the units of Sv/y per Bq/y and express all the radiological information about individual epository sites and ecosystems as a single, radionuclide-specific, number that relates geosphere releases to radiological dose. Further, we suggest a method for validating model parameters using data from field tracer tests. In two companion papers we present the underlying model framework for pathway analyses and a newly developed numerical module within the numerical software Ecolego Toolbox. Transport models

  18. Prediction of early death among patients enrolled in phase I trials: development and validation of a new model based on platelet count and albumin.

    Science.gov (United States)

    Ploquin, A; Olmos, D; Lacombe, D; A'Hern, R; Duhamel, A; Twelves, C; Marsoni, S; Morales-Barrera, R; Soria, J-C; Verweij, J; Voest, E E; Schöffski, P; Schellens, J H; Kramar, A; Kristeleit, R S; Arkenau, H-T; Kaye, S B; Penel, N

    2012-09-25

    Selecting patients with 'sufficient life expectancy' for Phase I oncology trials remains challenging. The Royal Marsden Hospital Score (RMS) previously identified high-risk patients as those with ≥ 2 of the following: albumin upper limit of normal; >2 metastatic sites. This study developed an alternative prognostic model, and compared its performance with that of the RMS. The primary end point was the 90-day mortality rate. The new model was developed from the same database as RMS, but it used Chi-squared Automatic Interaction Detection (CHAID). The ROC characteristics of both methods were then validated in an independent database of 324 patients enrolled in European Organization on Research and Treatment of Cancer Phase I trials of cytotoxic agents between 2000 and 2009. The CHAID method identified high-risk patients as those with albumin model and RMS, respectively. The negative predictive values (NPV) were similar for the CHAID model and RMS. The CHAID model and RMS provided a similarly high level of NPV, but the CHAID model gave a better accuracy in the validation set. Both CHAID model and RMS may improve the screening process in phase I trials.

  19. Development and validation of predictive simulation model of multi-layer repair welding process by temper bead technique

    International Nuclear Information System (INIS)

    Okano, Shigetaka; Miyasaka, Fumikazu; Mochizuki, Masahito; Tanaka, Manabu

    2015-01-01

    Stress corrosion cracking (SCC) has recently been observed in the nickel base alloy weld metal of dissimilar pipe joint used in pressurized water reactor (PWR) . Temper bead technique has been developed as one of repair procedures against SCC applicable in case that post weld heat treatment (PWHT) is difficult to carry out. In this regard, however it is essential to pass the property and performance qualification test to confirm the effect of tempering on the mechanical properties at repair welds before temper bead technique is actually used in practice. Thus the appropriate welding procedure conditions in temper bead technique are determined on the basis of the property and performance qualification testing. It is necessary for certifying the structural soundness and reliability at repair welds but takes a lot of work and time in the present circumstances. Therefore it is desirable to establish the reasonable alternatives for qualifying the property and performance at repair welds. In this study, mathematical modeling and numerical simulation procedures were developed for predicting weld bead configuration and temperature distribution during multi-layer repair welding process by temper bead technique. In the developed simulation technique, characteristics of heat source in temper bead welding are calculated from weld heat input conditions through the arc plasma simulation and then weld bead configuration and temperature distribution during temper bead welding are calculated from characteristics of heat source obtained through the coupling analysis between bead surface shape and thermal conduction. The simulation results were compared with the experimental results under the same welding heat input conditions. As the results, the bead surface shape and temperature distribution, such as A cl lines, were in good agreement between simulation and experimental results. It was concluded that the developed simulation technique has the potential to become useful for

  20. The Predictive Validity of Projective Measures.

    Science.gov (United States)

    Suinn, Richard M.; Oskamp, Stuart

    Written for use by clinical practitioners as well as psychological researchers, this book surveys recent literature (1950-1965) on projective test validity by reviewing and critically evaluating studies which shed light on what may reliably be predicted from projective test results. Two major instruments are covered: the Rorschach and the Thematic…

  1. Configuration and validation of an analytical model predicting secondary neutron radiation in proton therapy using Monte Carlo simulations and experimental measurements.

    Science.gov (United States)

    Farah, J; Bonfrate, A; De Marzi, L; De Oliveira, A; Delacroix, S; Martinetti, F; Trompier, F; Clairand, I

    2015-05-01

    This study focuses on the configuration and validation of an analytical model predicting leakage neutron doses in proton therapy. Using Monte Carlo (MC) calculations, a facility-specific analytical model was built to reproduce out-of-field neutron doses while separately accounting for the contribution of intra-nuclear cascade, evaporation, epithermal and thermal neutrons. This model was first trained to reproduce in-water neutron absorbed doses and in-air neutron ambient dose equivalents, H*(10), calculated using MCNPX. Its capacity in predicting out-of-field doses at any position not involved in the training phase was also checked. The model was next expanded to enable a full 3D mapping of H*(10) inside the treatment room, tested in a clinically relevant configuration and finally consolidated with experimental measurements. Following the literature approach, the work first proved that it is possible to build a facility-specific analytical model that efficiently reproduces in-water neutron doses and in-air H*(10) values with a maximum difference less than 25%. In addition, the analytical model succeeded in predicting out-of-field neutron doses in the lateral and vertical direction. Testing the analytical model in clinical configurations proved the need to separate the contribution of internal and external neutrons. The impact of modulation width on stray neutrons was found to be easily adjustable while beam collimation remains a challenging issue. Finally, the model performance agreed with experimental measurements with satisfactory results considering measurement and simulation uncertainties. Analytical models represent a promising solution that substitutes for time-consuming MC calculations when assessing doses to healthy organs. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  2. Mortality in severe trauma patients attended by emergency services in Navarre, Spain: validation of a new prediction model and comparison with the Revised Injury Severity Classification Score II.

    Science.gov (United States)

    Ali Ali, Bismil; Lefering, Rolf; Fortún Moral, Mariano; Belzunegui Otano, Tomás

    2018-01-01

    To validate the Mortality Prediction Model of Navarre (MPMN) to predict death after severe trauma and compare it to the Revised Injury Severity Classification Score II (RISCII). Retrospective analysis of a cohort of severe trauma patients (New Injury Severity Score >15) who were attended by emergency services in the Spanish autonomous community of Navarre between 2013 and 2015. The outcome variable was 30-day all-cause mortality. Risk was calculated with the MPMN and the RISCII. The performance of each model was assessed with the area under the receiver operating characteristic (ROC) curve and precision with respect to observed mortality. Calibration was assessed with the Hosmer-Lemeshow test. We included 516 patients. The mean (SD) age was 56 (23) years, and 363 (70%) were males. Ninety patients (17.4%) died within 30 days. The 30-day mortality rates predicted by the MPMN and RISCII were 16.4% and 15.4%, respectively. The areas under the ROC curves were 0.925 (95% CI, 0.902-0.952) for the MPMN and 0.941 (95% CI, 0.921-0.962) for the RISCII (P=0.269, DeLong test). Calibration statistics were 13.6 (P=.09) for the MPMN and 8.9 (P=.35) for the RISCII. Both the MPMN and the RISCII show good ability to discriminate risk and predict 30-day all-cause mortality in severe trauma patients.

  3. Computational Model Prediction and Biological Validation Using Simplified Mixed Field Exposures for the Development of a GCR Reference Field

    Science.gov (United States)

    Hada, M.; Rhone, J.; Beitman, A.; Saganti, P.; Plante, I.; Ponomarev, A.; Slaba, T.; Patel, Z.

    2018-01-01

    The yield of chromosomal aberrations has been shown to increase in the lymphocytes of astronauts after long-duration missions of several months in space. Chromosome exchanges, especially translocations, are positively correlated with many cancers and are therefore a potential biomarker of cancer risk associated with radiation exposure. Although extensive studies have been carried out on the induction of chromosomal aberrations by low- and high-LET radiation in human lymphocytes, fibroblasts, and epithelial cells exposed in vitro, there is a lack of data on chromosome aberrations induced by low dose-rate chronic exposure and mixed field beams such as those expected in space. Chromosome aberration studies at NSRL will provide the biological validation needed to extend the computational models over a broader range of experimental conditions (more complicated mixed fields leading up to the galactic cosmic rays (GCR) simulator), helping to reduce uncertainties in radiation quality effects and dose-rate dependence in cancer risk models. These models can then be used to answer some of the open questions regarding requirements for a full GCR reference field, including particle type and number, energy, dose rate, and delivery order. In this study, we designed a simplified mixed field beam with a combination of proton, helium, oxygen, and iron ions with shielding or proton, helium, oxygen, and titanium without shielding. Human fibroblasts cells were irradiated with these mixed field beam as well as each single beam with acute and chronic dose rate, and chromosome aberrations (CA) were measured with 3-color fluorescent in situ hybridization (FISH) chromosome painting methods. Frequency and type of CA induced with acute dose rate and chronic dose rates with single and mixed field beam will be discussed. A computational chromosome and radiation-induced DNA damage model, BDSTRACKS (Biological Damage by Stochastic Tracks), was updated to simulate various types of CA induced by

  4. Validation of the 2014 European Society of Cardiology Sudden Cardiac Death Risk Prediction Model in Hypertrophic Cardiomyopathy in a Reference Center in South America.

    Science.gov (United States)

    Fernández, Adrián; Quiroga, Alejandro; Ochoa, Juan Pablo; Mysuta, Mauricio; Casabé, José Horacio; Biagetti, Marcelo; Guevara, Eduardo; Favaloro, Liliana E; Fava, Agostina M; Galizio, Néstor

    2016-07-01

    Sudden cardiac death (SCD) is a common cause of death in hypertrophic cardiomyopathy (HC). Our aim was to conduct an external and independent validation in South America of the 2014 European Society of Cardiology (ESC) SCD risk prediction model to identify patients requiring an implantable cardioverter defibrillator. This study included 502 consecutive patients with HC followed from March, 1993 to December, 2014. A combined end point of SCD or appropriate implantable cardioverter defibrillator therapy was assessed. For the quantitative estimation of individual 5-year SCD risk, we used the formula: 1 - 0.998(exp(Prognostic index)). Our database also included the abnormal blood pressure response to exercise as a risk marker. We analyzed the 3 categories of 5-year risk proposed by the ESC: low risk (LR) validated in our population and represents an improvement compared with previous approaches. A larger multicenter, independent and external validation of the model with long-term follow-up would be advisable. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Validation through model testing

    International Nuclear Information System (INIS)

    1995-01-01

    Geoval-94 is the third Geoval symposium arranged jointly by the OECD/NEA and the Swedish Nuclear Power Inspectorate. Earlier symposia in this series took place in 1987 and 1990. In many countries, the ongoing programmes to site and construct deep geological repositories for high and intermediate level nuclear waste are close to realization. A number of studies demonstrates the potential barrier function of the geosphere, but also that there are many unresolved issues. A key to these problems are the possibilities to gain knowledge by model testing with experiments and to increase confidence in models used for prediction. The sessions cover conclusions from the INTRAVAL-project, experiences from integrated experimental programs and underground research laboratories as well as the integration between performance assessment and site characterisation. Technical issues ranging from waste and buffer interactions with the rock to radionuclide migration in different geological media is addressed. (J.S.)

  6. The development and validation of a clinical prediction model to determine the probability of MODY in patients with young-onset diabetes.

    Science.gov (United States)

    Shields, B M; McDonald, T J; Ellard, S; Campbell, M J; Hyde, C; Hattersley, A T

    2012-05-01

    Diagnosing MODY is difficult. To date, selection for molecular genetic testing for MODY has used discrete cut-offs of limited clinical characteristics with varying sensitivity and specificity. We aimed to use multiple, weighted, clinical criteria to determine an individual's probability of having MODY, as a crucial tool for rational genetic testing. We developed prediction models using logistic regression on data from 1,191 patients with MODY (n = 594), type 1 diabetes (n = 278) and type 2 diabetes (n = 319). Model performance was assessed by receiver operating characteristic (ROC) curves, cross-validation and validation in a further 350 patients. The models defined an overall probability of MODY using a weighted combination of the most discriminative characteristics. For MODY, compared with type 1 diabetes, these were: lower HbA(1c), parent with diabetes, female sex and older age at diagnosis. MODY was discriminated from type 2 diabetes by: lower BMI, younger age at diagnosis, female sex, lower HbA(1c), parent with diabetes, and not being treated with oral hypoglycaemic agents or insulin. Both models showed excellent discrimination (c-statistic = 0.95 and 0.98, respectively), low rates of cross-validated misclassification (9.2% and 5.3%), and good performance on the external test dataset (c-statistic = 0.95 and 0.94). Using the optimal cut-offs, the probability models improved the sensitivity (91% vs 72%) and specificity (94% vs 91%) for identifying MODY compared with standard criteria of diagnosis MODY. This allows an improved and more rational approach to determine who should have molecular genetic testing.

  7. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites: TL-LUE Parameterization and Validation

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Yanlian [Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing China; Joint Center for Global Change Studies, Beijing China; Wu, Xiaocui [International Institute for Earth System Sciences, Nanjing University, Nanjing China; Joint Center for Global Change Studies, Beijing China; Ju, Weimin [International Institute for Earth System Sciences, Nanjing University, Nanjing China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing China; Chen, Jing M. [International Institute for Earth System Sciences, Nanjing University, Nanjing China; Joint Center for Global Change Studies, Beijing China; Wang, Shaoqiang [Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing China; Wang, Huimin [Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing China; Yuan, Wenping [State Key Laboratory of Earth Surface Processes and Resource Ecology, Future Earth Research Institute, Beijing Normal University, Beijing China; Andrew Black, T. [Faculty of Land and Food Systems, University of British Columbia, Vancouver British Columbia Canada; Jassal, Rachhpal [Faculty of Land and Food Systems, University of British Columbia, Vancouver British Columbia Canada; Ibrom, Andreas [Department of Environmental Engineering, Technical University of Denmark (DTU), Kgs. Lyngby Denmark; Han, Shijie [Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang China; Yan, Junhua [South China Botanical Garden, Chinese Academy of Sciences, Guangzhou China; Margolis, Hank [Centre for Forest Studies, Faculty of Forestry, Geography and Geomatics, Laval University, Quebec City Quebec Canada; Roupsard, Olivier [CIRAD-Persyst, UMR Ecologie Fonctionnelle and Biogéochimie des Sols et Agroécosystèmes, SupAgro-CIRAD-INRA-IRD, Montpellier France; CATIE (Tropical Agricultural Centre for Research and Higher Education), Turrialba Costa Rica; Li, Yingnian [Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining China; Zhao, Fenghua [Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing China; Kiely, Gerard [Environmental Research Institute, Civil and Environmental Engineering Department, University College Cork, Cork Ireland; Starr, Gregory [Department of Biological Sciences, University of Alabama, Tuscaloosa Alabama USA; Pavelka, Marian [Laboratory of Plants Ecological Physiology, Institute of Systems Biology and Ecology AS CR, Prague Czech Republic; Montagnani, Leonardo [Forest Services, Autonomous Province of Bolzano, Bolzano Italy; Faculty of Sciences and Technology, Free University of Bolzano, Bolzano Italy; Wohlfahrt, Georg [Institute for Ecology, University of Innsbruck, Innsbruck Austria; European Academy of Bolzano, Bolzano Italy; D' Odorico, Petra [Grassland Sciences Group, Institute of Agricultural Sciences, ETH Zurich Switzerland; Cook, David [Atmospheric and Climate Research Program, Environmental Science Division, Argonne National Laboratory, Argonne Illinois USA; Arain, M. Altaf [McMaster Centre for Climate Change and School of Geography and Earth Sciences, McMaster University, Hamilton Ontario Canada; Bonal, Damien [INRA Nancy, UMR EEF, Champenoux France; Beringer, Jason [School of Earth and Environment, The University of Western Australia, Crawley Australia; Blanken, Peter D. [Department of Geography, University of Colorado Boulder, Boulder Colorado USA; Loubet, Benjamin [UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon France; Leclerc, Monique Y. [Department of Crop and Soil Sciences, College of Agricultural and Environmental Sciences, University of Georgia, Athens Georgia USA; Matteucci, Giorgio [Viea San Camillo Ed LellisViterbo, University of Tuscia, Viterbo Italy; Nagy, Zoltan [MTA-SZIE Plant Ecology Research Group, Szent Istvan University, Godollo Hungary; Olejnik, Janusz [Meteorology Department, Poznan University of Life Sciences, Poznan Poland; Department of Matter and Energy Fluxes, Global Change Research Center, Brno Czech Republic; Paw U, Kyaw Tha [Department of Land, Air and Water Resources, University of California, Davis California USA; Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge USA; Varlagin, Andrej [A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow Russia

    2016-04-01

    We present the first extended validation of satellitemicrowave (MW) liquidwater path (LWP) for low nonprecipitating clouds, from four operational sensors, against ship-borne observations from a three-channel MW radiometer collected along ship transects over the northeast Pacific during May–August 2013. Satellite MW retrievals have an overall correlation of 0.84 with ship observations and a bias of 9.3 g/m2. The bias for broken cloud scenes increases linearly with water vapor path and remains below 17.7 g/m2. In contrast, satelliteMWLWP is unbiased in overcast scenes with correlations up to 0.91, demonstrating that the retrievals are accurate and reliable under these conditions. Satellite MW retrievals produce a diurnal cycle amplitude consistent with ship-based observations (33 g/m2). Observations taken aboard extended ship cruises to evaluate not only satellite MW LWP but also LWP derived from visible/infrared sensors offer a new way to validate this important property over vast oceanic regions.

  8. An analytical model for the prediction of fluid-elastic forces in a rod bundle subjected to axial flow: theory, experimental validation and application to PWR fuel assemblies

    International Nuclear Information System (INIS)

    Beaud, F.

    1997-01-01

    A model predicting the fluid-elastic forces in a bundle of circular cylinders subjected to axial flow is presented in this paper. Whereas previously published models were limited to circular flow channel, the present one allows to take a rectangular flow external boundary into account. For that purpose, an original approach is derived from the standard method of images. This model will eventually be used to predict the fluid-structure coupling between the flow of primary coolant and a fuel assemblies in PWR nuclear reactors. It is indeed of major importance since the flow is shown to induce quite high damping and could therefore mitigate the incidence of an external load like a seismic excitation on the dynamics of the assemblies. The proposed model is validated on two cases from the literature but still needs further comparisons with the experiments being currently carried out on the EDF set-up. The flow has been shown to induce an approximate 12% damping on a PWR fuel assembly, at nominal reactor conditions. The possible grid effect on the fluid-structure coupling has been neglected so far but will soon be investigated at EDF. (author)

  9. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites: TL-LUE Parameterization and Validation

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Yanlian [Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing China; Joint Center for Global Change Studies, Beijing China; Wu, Xiaocui [International Institute for Earth System Sciences, Nanjing University, Nanjing China; Joint Center for Global Change Studies, Beijing China; Ju, Weimin [International Institute for Earth System Sciences, Nanjing University, Nanjing China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing China; Chen, Jing M. [International Institute for Earth System Sciences, Nanjing University, Nanjing China; Joint Center for Global Change Studies, Beijing China; Wang, Shaoqiang [Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing China; Wang, Huimin [Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing China; Yuan, Wenping [State Key Laboratory of Earth Surface Processes and Resource Ecology, Future Earth Research Institute, Beijing Normal University, Beijing China; Andrew Black, T. [Faculty of Land and Food Systems, University of British Columbia, Vancouver British Columbia Canada; Jassal, Rachhpal [Faculty of Land and Food Systems, University of British Columbia, Vancouver British Columbia Canada; Ibrom, Andreas [Department of Environmental Engineering, Technical University of Denmark (DTU), Kgs. Lyngby Denmark; Han, Shijie [Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang China; Yan, Junhua [South China Botanical Garden, Chinese Academy of Sciences, Guangzhou China; Margolis, Hank [Centre for Forest Studies, Faculty of Forestry, Geography and Geomatics, Laval University, Quebec City Quebec Canada; Roupsard, Olivier [CIRAD-Persyst, UMR Ecologie Fonctionnelle and Biogéochimie des Sols et Agroécosystèmes, SupAgro-CIRAD-INRA-IRD, Montpellier France; CATIE (Tropical Agricultural Centre for Research and Higher Education), Turrialba Costa Rica; Li, Yingnian [Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining China; Zhao, Fenghua [Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing China; Kiely, Gerard [Environmental Research Institute, Civil and Environmental Engineering Department, University College Cork, Cork Ireland; Starr, Gregory [Department of Biological Sciences, University of Alabama, Tuscaloosa Alabama USA; Pavelka, Marian [Laboratory of Plants Ecological Physiology, Institute of Systems Biology and Ecology AS CR, Prague Czech Republic; Montagnani, Leonardo [Forest Services, Autonomous Province of Bolzano, Bolzano Italy; Faculty of Sciences and Technology, Free University of Bolzano, Bolzano Italy; Wohlfahrt, Georg [Institute for Ecology, University of Innsbruck, Innsbruck Austria; European Academy of Bolzano, Bolzano Italy; D' Odorico, Petra [Grassland Sciences Group, Institute of Agricultural Sciences, ETH Zurich Switzerland; Cook, David [Atmospheric and Climate Research Program, Environmental Science Division, Argonne National Laboratory, Argonne Illinois USA; Arain, M. Altaf [McMaster Centre for Climate Change and School of Geography and Earth Sciences, McMaster University, Hamilton Ontario Canada; Bonal, Damien [INRA Nancy, UMR EEF, Champenoux France; Beringer, Jason [School of Earth and Environment, The University of Western Australia, Crawley Australia; Blanken, Peter D. [Department of Geography, University of Colorado Boulder, Boulder Colorado USA; Loubet, Benjamin [UMR ECOSYS, INRA, AgroParisTech, Université Paris-Saclay, Thiverval-Grignon France; Leclerc, Monique Y. [Department of Crop and Soil Sciences, College of Agricultural and Environmental Sciences, University of Georgia, Athens Georgia USA; Matteucci, Giorgio [Viea San Camillo Ed LellisViterbo, University of Tuscia, Viterbo Italy; Nagy, Zoltan [MTA-SZIE Plant Ecology Research Group, Szent Istvan University, Godollo Hungary; Olejnik, Janusz [Meteorology Department, Poznan University of Life Sciences, Poznan Poland; Department of Matter and Energy Fluxes, Global Change Research Center, Brno Czech Republic; Paw U, Kyaw Tha [Department of Land, Air and Water Resources, University of California, Davis California USA; Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge USA; Varlagin, Andrej [A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow Russia

    2016-04-06

    Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at 6 FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8-day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems and it is more robust with regard to usual biases in input data than existing approaches which neglect the bi-modal within-canopy distribution of PAR.

  10. Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Perandini, Simone; Soardi, G.A.; Signorini, M.; Motton, M.; Montemezzi, S. [Azienda Ospedaliera Universitaria Integrata di Verona, UOC Radiologia, Ospedale Maggiore di Borgo Trento, Verona (Italy); Larici, A.R.; Del Ciello, A. [Universita Cattolica del Sacro Cuore, Dipartimento di Scienze Radiologiche, Roma (Italy); Rizzardi, G. [Ospedale Humanitas Gavazzeni, UO Chirurgia Toracica, Bergamo (Italy); Solazzo, A. [Ospedale Humanitas Gavazzeni, UO Radiologia, Bergamo (Italy); Mancino, L.; Zeraj, F. [Ospedale dell' Angelo di Mestre, UO Pneumologia, Venezia (Italy); Bernhart, M. [Ospedale dell' Angelo di Mestre, UO Radiologia, Venezia (Italy)

    2017-05-15

    To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models. Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines. Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742-0.862) and for the BIMC model of 0.822 (95 % CI 0.758-0.875). Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis. (orig.)

  11. Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation

    International Nuclear Information System (INIS)

    Perandini, Simone; Soardi, G.A.; Signorini, M.; Motton, M.; Montemezzi, S.; Larici, A.R.; Del Ciello, A.; Rizzardi, G.; Solazzo, A.; Mancino, L.; Zeraj, F.; Bernhart, M.

    2017-01-01

    To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models. Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines. Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742-0.862) and for the BIMC model of 0.822 (95 % CI 0.758-0.875). Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis. (orig.)

  12. Disentangling the Predictive Validity of High School Grades for Academic Success in University

    Science.gov (United States)

    Vulperhorst, Jonne; Lutz, Christel; de Kleijn, Renske; van Tartwijk, Jan

    2018-01-01

    To refine selective admission models, we investigate which measure of prior achievement has the best predictive validity for academic success in university. We compare the predictive validity of three core high school subjects to the predictive validity of high school grade point average (GPA) for academic achievement in a liberal arts university…

  13. Development and validation of a terrestrial biotic ligand model predicting the effect of cobalt on root growth of barley (Hordeum vulgare)

    International Nuclear Information System (INIS)

    Lock, K.; De Schamphelaere, K.A.C.; Becaus, S.; Criel, P.; Van Eeckhout, H.; Janssen, C.R.

    2007-01-01

    A Biotic Ligand Model was developed predicting the effect of cobalt on root growth of barley (Hordeum vulgare) in nutrient solutions. The extent to which Ca 2+ , Mg 2+ , Na + , K + ions and pH independently affect cobalt toxicity to barley was studied. With increasing activities of Mg 2+ , and to a lesser extent also K + , the 4-d EC50 Co2+ increased linearly, while Ca 2+ , Na + and H + activities did not affect Co 2+ toxicity. Stability constants for the binding of Co 2+ , Mg 2+ and K + to the biotic ligand were obtained: log K CoBL = 5.14, log K MgBL = 3.86 and log K KBL = 2.50. Limited validation of the model with one standard artificial soil and one standard field soil showed that the 4-d EC50 Co2+ could only be predicted within a factor of four from the observed values, indicating further refinement of the BLM is needed. - Biotic Ligand Models are not only a useful tool to assess metal toxicity in aquatic systems but can also be used for terrestrial plants

  14. Modelling the deposition of airborne radionuclides into the urban environment. First report of the VAMP Urban Working Group. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    1994-08-01

    A co-ordinated research programme was begun at the IAEA in 1988 with the short title of Validation of Environmental Model Predictions (VAMP). The VAMP Urban Working Group aims to examine, by means of expert review combined with formal validation exercises, modelling for the assessment of the radiation exposure of urban populations through the external irradiation and inhalation pathways. An aim of the studies is to evaluate the lessons learned and to document the improvements in modelling capability as a result of experience gained following the Chernobyl accident. This Technical Document, the first report of the Group, addresses the subject of the deposition of airborne radionuclides into the urban environment. It summarizes not only the present status of modelling in this field, but also the results of a limited validation exercise that was performed under the auspices of VAMP. 42 refs, figs and tabs

  15. Predictive validity of the Slovene Matura

    Directory of Open Access Journals (Sweden)

    Valentin Bucik

    2001-09-01

    Full Text Available Passing Matura is the last step of the secondary school graduation, but it is also the entrance ticket for the university. Besides, the summary score of Matura exam takes part in the selection process for the particular university studies in case of 'numerus clausus'. In discussing either aim of Matura important dilemmas arise, namely, is the Matura examination sufficiently exact and rightful procedure to, firstly, use its results for settling starting studying conditions and, secondly, to select validly, reliably and sensibly the best candidates for university studies. There are some questions concerning predictive validity of Matura that should be answered, e.g. (i does Matura as an enrollment procedure add to the qualitaty of the study; (ii is it a better selection tool than entrance examinations formerly used in different faculties in the case of 'numerus clausus'; and (iii is it reasonable to expect high predictive validity of Matura results for success at the university at all. Recent results show that in the last few years the dropout-rate is lower than before, the pass-rate between the first and the second year is higher and the average duration of study per student is shorter. It is clear, however, that it is not possible to simply predict the study success from the Matura results. There are too many factors influencing the success in the university studies. In most examined study programs the correlation between Matura results and study success is positive but moderate, therefore it can not be said categorically that only candidates accepted according to the Matura results are (or will be the best students. Yet it has been shown that Matura is a standardized procedure, comparable across different candidates entering university, and that – when compared entrance examinations – it is more objective, reliable, and hen ce more valid and fair a procedure. In addition, comparable procedures of university recruiting and selection can be

  16. Experimental validation of the twins prediction program for rolling noise. Pt.1: description of the model and method

    NARCIS (Netherlands)

    Thompson, D.J.; Hemsworth, B.; Vincent, N.

    1996-01-01

    The C163 Expert Committee of the European Rail Research Institute (ERRI) concerned with Railway Noise, has been developing theoretical models for the generation of wheel/rail rolling noise. These models have been brought together into a software package, called TWINS ("Track-Wheel Interaction Noise

  17. Modelling of the transfer of radiocaesium from deposition to lake ecosystems. Report of the VAMP aquatic working group. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    2000-03-01

    The environmental impact of releases of radionuclides from nuclear installations can be predicted using assessment models. For such assessments information on their reliability must be provided. Ideally models should be developed and tested using actual data on the transfer of the nuclides which are site specific for the environment being modelled. In the past, generic data have often been taken from environmental contamination that resulted from the fallout from the nuclear weapons testing in the 1950s and 1960s or from laboratory experiments. However, it has always been recognized that there may be differences in the physico-chemical form of the radionuclides from these sources as compared to those that could be released from nuclear installations. Furthermore, weapons fallout was spread over time; it did not provide a single pulse which is generally used in testing models that predict time dependence. On the other hand, the Chernobyl accident resulted in a single pulse, which was detected and measured in a variety of environments throughout Europe. The acquisition of these new data sets justified the establishment of an international programme aimed at collating data from different IAEA Member States and at co-ordinating work on new model testing studies. The IAEA established a Co-ordinated Research Programme (CRP) on 'Validation of Environmental Model Predictions' (VAMP). The principal objectives of the VAMP Co-ordinated Research Programme were: (a) To facilitate the validation of assessment models for radionuclide transfer in the terrestrial, aquatic and urban environments. It is envisaged that this will be achieved by acquiring suitable sets of environmental data from the results of the national research and monitoring programmes established following the Chernobyl release. (b) To guide, if necessary, environmental research and monitoring efforts to acquire data for the validation of models used to assess the most significant radiological exposure pathways

  18. Validating spatiotemporal predictions of an important pest of small grains.

    Science.gov (United States)

    Merrill, Scott C; Holtzer, Thomas O; Peairs, Frank B; Lester, Philip J

    2015-01-01

    Arthropod pests are typically managed using tactics applied uniformly to the whole field. Precision pest management applies tactics under the assumption that within-field pest pressure differences exist. This approach allows for more precise and judicious use of scouting resources and management tactics. For example, a portion of a field delineated as attractive to pests may be selected to receive extra monitoring attention. Likely because of the high variability in pest dynamics, little attention has been given to developing precision pest prediction models. Here, multimodel synthesis was used to develop a spatiotemporal model predicting the density of a key pest of wheat, the Russian wheat aphid, Diuraphis noxia (Kurdjumov). Spatially implicit and spatially explicit models were synthesized to generate spatiotemporal pest pressure predictions. Cross-validation and field validation were used to confirm model efficacy. A strong within-field signal depicting aphid density was confirmed with low prediction errors. Results show that the within-field model predictions will provide higher-quality information than would be provided by traditional field scouting. With improvements to the broad-scale model component, the model synthesis approach and resulting tool could improve pest management strategy and provide a template for the development of spatially explicit pest pressure models. © 2014 Society of Chemical Industry.

  19. Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait--a cohort study.

    Science.gov (United States)

    Farran, Bassam; Channanath, Arshad Mohamed; Behbehani, Kazem; Thanaraj, Thangavel Alphonse

    2013-05-14

    We build classification models and risk assessment tools for diabetes, hypertension and comorbidity using machine-learning algorithms on data from Kuwait. We model the increased proneness in diabetic patients to develop hypertension and vice versa. We ascertain the importance of ethnicity (and natives vs expatriate migrants) and of using regional data in risk assessment. Retrospective cohort study. Four machine-learning techniques were used: logistic regression, k-nearest neighbours (k-NN), multifactor dimensionality reduction and support vector machines. The study uses fivefold cross validation to obtain generalisation accuracies and errors. Kuwait Health Network (KHN) that integrates data from primary health centres and hospitals in Kuwait. 270 172 hospital visitors (of which, 89 858 are diabetic, 58 745 hypertensive and 30 522 comorbid) comprising Kuwaiti natives, Asian and Arab expatriates. Incident type 2 diabetes, hypertension and comorbidity. Classification accuracies of >85% (for diabetes) and >90% (for hypertension) are achieved using only simple non-laboratory-based parameters. Risk assessment tools based on k-NN classification models are able to assign 'high' risk to 75% of diabetic patients and to 94% of hypertensive patients. Only 5% of diabetic patients are seen assigned 'low' risk. Asian-specific models and assessments perform even better. Pathological conditions of diabetes in the general population or in hypertensive population and those of hypertension are modelled. Two-stage aggregate classification models and risk assessment tools, built combining both the component models on diabetes (or on hypertension), perform better than individual models. Data on diabetes, hypertension and comorbidity from the cosmopolitan State of Kuwait are available for the first time. This enabled us to apply four different case-control models to assess risks. These tools aid in the preliminary non-intrusive assessment of the population. Ethnicity is seen significant

  20. The validity of the potential model in predicting the structural, dynamical, thermodynamic properties of the unary and binary mixture of water-alcohol: Methanol-water case

    Science.gov (United States)

    Obeidat, Abdalla; Abu-Ghazleh, Hind

    2018-06-01

    Two intermolecular potential models of methanol (TraPPE-UA and OPLS-AA) have been used in order to examine their validity in reproducing the selected structural, dynamical, and thermodynamic properties in the unary and binary systems. These two models are combined with two water models (SPC/E and TIP4P). The temperature dependence of density, surface tension, diffusion and structural properties for the unary system has been computed over specific range of temperatures (200-300K). The very good performance of the TraPPE-UA potential model in predicting surface tension, diffusion, structure, and density of the unary system led us to examine its accuracy and performance in its aqueous solution. In the binary system the same properties were examined, using different mole fractions of methanol. The TraPPE-UA model combined with TIP4P-water shows a very good agreement with the experimental results for density and surface tension properties; whereas the OPLS-AA combined with SPCE-water shows a very agreement with experimental results regarding the diffusion coefficients. Two different approaches have been used in calculating the diffusion coefficient in the mixture, namely the Einstein equation (EE) and Green-Kubo (GK) method. Our results show the advantageous of applying GK over EE in reproducing the experimental results and in saving computer time.

  1. Validation of models with multivariate output

    International Nuclear Information System (INIS)

    Rebba, Ramesh; Mahadevan, Sankaran

    2006-01-01

    This paper develops metrics for validating computational models with experimental data, considering uncertainties in both. A computational model may generate multiple response quantities and the validation experiment might yield corresponding measured values. Alternatively, a single response quantity may be predicted and observed at different spatial and temporal points. Model validation in such cases involves comparison of multiple correlated quantities. Multiple univariate comparisons may give conflicting inferences. Therefore, aggregate validation metrics are developed in this paper. Both classical and Bayesian hypothesis testing are investigated for this purpose, using multivariate analysis. Since, commonly used statistical significance tests are based on normality assumptions, appropriate transformations are investigated in the case of non-normal data. The methodology is implemented to validate an empirical model for energy dissipation in lap joints under dynamic loading

  2. Validation of the 2014 European Society of Cardiology guidelines risk prediction model for the primary prevention of sudden cardiac death in hypertrophic cardiomyopathy.

    Science.gov (United States)

    Vriesendorp, Pieter A; Schinkel, Arend F L; Liebregts, Max; Theuns, Dominic A M J; van Cleemput, Johan; Ten Cate, Folkert J; Willems, Rik; Michels, Michelle

    2015-08-01

    The recently released 2014 European Society of Cardiology guidelines of hypertrophic cardiomyopathy (HCM) use a new clinical risk prediction model for sudden cardiac death (SCD), based on the HCM Risk-SCD study. Our study is the first external and independent validation of this new risk prediction model. The study population consisted of a consecutive cohort of 706 patients with HCM without prior SCD event, from 2 tertiary referral centers. The primary end point was a composite of SCD and appropriate implantable cardioverter-defibrillator therapy, identical to the HCM Risk-SCD end point. The 5-year SCD risk was calculated using the HCM Risk-SCD formula. Receiver operating characteristic curves and C-statistics were calculated for the 2014 European Society of Cardiology guidelines, and risk stratification methods of the 2003 American College of Cardiology/European Society of Cardiology guidelines and 2011 American College of Cardiology Foundation/American Heart Association guidelines. During follow-up of 7.7±5.3 years, SCD occurred in 42 (5.9%) of 706 patients (ages 49±16 years; 34% women). The C-statistic of the new model was 0.69 (95% CI, 0.57-0.82; P=0.008), which performed significantly better than the conventional risk factor models based on the 2003 guidelines (C-statistic of 0.55: 95% CI, 0.47-0.63; P=0.3), and 2011 guidelines (C-statistic of 0.60: 95% CI, 0.50-0.70; P=0.07). The HCM Risk-SCD model improves the risk stratification of patients with HCM for primary prevention of SCD, and calculating an individual risk estimate contributes to the clinical decision-making process. Improved risk stratification is important for the decision making before implantable cardioverter-defibrillator implantation for the primary prevention of SCD. © 2015 American Heart Association, Inc.

  3. A field guide to predict delayed mortality of fire-damaged ponderosa pine: application and validation of the Malheur model.

    Science.gov (United States)

    Walter G. Thies; Douglas J. Westlind; Mark Loewen; Greg. Brenner

    2008-01-01

    The Malheur model for fire-caused delayed mortality is presented as an easily interpreted graph (mortality-probability calculator) as part of a one-page field guide that allows the user to determine postfire probability of mortality for ponderosa pine (Pinus ponderosa Dougl. ex Laws.). Following both prescribed burns and wildfires, managers need...

  4. Predictive modeling of complications.

    Science.gov (United States)

    Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

    2016-09-01

    Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

  5. Establishing model credibility involves more than validation

    International Nuclear Information System (INIS)

    Kirchner, T.

    1991-01-01

    One widely used definition of validation is that the quantitative test of the performance of a model through the comparison of model predictions to independent sets of observations from the system being simulated. The ability to show that the model predictions compare well with observations is often thought to be the most rigorous test that can be used to establish credibility for a model in the scientific community. However, such tests are only part of the process used to establish credibility, and in some cases may be either unnecessary or misleading. Naylor and Finger extended the concept of validation to include the establishment of validity for the postulates embodied in the model and the test of assumptions used to select postulates for the model. Validity of postulates is established through concurrence by experts in the field of study that the mathematical or conceptual model contains the structural components and mathematical relationships necessary to adequately represent the system with respect to the goals for the model. This extended definition of validation provides for consideration of the structure of the model, not just its performance, in establishing credibility. Evaluation of a simulation model should establish the correctness of the code and the efficacy of the model within its domain of applicability. (24 refs., 6 figs.)

  6. A model of prediction and cross-validation of fat-free mass in men with motor complete spinal cord injury.

    Science.gov (United States)

    Gorgey, Ashraf S; Dolbow, David R; Gater, David R

    2012-07-01

    To establish and validate prediction equations by using body weight to predict legs, trunk, and whole-body fat-free mass (FFM) in men with chronic complete spinal cord injury (SCI). Cross-sectional design. Research setting in a large medical center. Individuals with SCI (N=63) divided into prediction (n=42) and cross-validation (n=21) groups. Not applicable. Whole-body FFM and regional FFM were determined by using dual-energy x-ray absorptiometry. Body weight was measured by using a wheelchair weighing scale after subtracting the weight of the chair. Body weight predicted legs FFM (legs FFM=.09×body weight+6.1; R(2)=.25, standard error of the estimate [SEE]=3.1kg, PFFM (trunk FFM=.21×body weight+8.6; R(2)=.56, SEE=3.6kg, PFFM (whole-body FFM=.288×body weight+26.3; R(2)=.53, SEE=5.3kg, PFFM(predicted) (FFM predicted from the derived equations) shared 86% of the variance in whole-body FFM(measured) (FFM measured using dual-energy x-ray absorptiometry scan) (R(2)=.86, SEE=1.8kg, PFFM(measured), and 66% of legs FFM(measured). The trunk FFM(predicted) shared 69% of the variance in trunk FFM(measured) (R(2)=.69, SEE=2.7kg, PFFM(predicted) shared 67% of the variance in legs FFM(measured) (R(2)=.67, SEE=2.8kg, PFFM did not differ between the prediction and validation groups. Body weight can be used to predict whole-body FFM and regional FFM. The predicted whole-body FFM improved the prediction of trunk FFM and legs FFM. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  7. Validating Animal Models

    Directory of Open Access Journals (Sweden)

    Nina Atanasova

    2015-06-01

    Full Text Available In this paper, I respond to the challenge raised against contemporary experimental neurobiology according to which the field is in a state of crisis because of the multiple experimental protocols employed in different laboratories and strengthening their reliability that presumably preclude the validity of neurobiological knowledge. I provide an alternative account of experimentation in neurobiology which makes sense of its experimental practices. I argue that maintaining a multiplicity of experimental protocols and strengthening their reliability are well justified and they foster rather than preclude the validity of neurobiological knowledge. Thus, their presence indicates thriving rather than crisis of experimental neurobiology.

  8. Predictive Modeling in Race Walking

    Directory of Open Access Journals (Sweden)

    Krzysztof Wiktorowicz

    2015-01-01

    Full Text Available This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.

  9. Operational validation of a multi-period and multi-criteria model conditioning approach for the prediction of rainfall-runoff processes in small forest catchments

    Science.gov (United States)

    Choi, H.; Kim, S.

    2012-12-01

    Limestone. The study is progressed based on the followings. Firstly, hydrological time series of each catchment are sampled and clustered into multi-period having distinctly different temporal characteristics, and secondly, behavioural parameter distributions are determined in each multi-period based on the specification of multi-criteria model performance measures. Finally, behavioural parameter sets of each multi-period of single catchment are applied on the corresponding period of other catchments, and the cross-validations are conducted in this manner for all catchments The multi-period model conditioning approach is clearly effective to reduce the width of prediction limits, giving better model performance against the temporal variability of hydrological characteristics, and has enough potential to be the effective prediction tool for ungauged catchments. However, more advanced and continuous studies are needed to expand the application of this approach in prediction of hydrological responses in ungauged catchments,

  10. Geochemistry Model Validation Report: External Accumulation Model

    International Nuclear Information System (INIS)

    Zarrabi, K.

    2001-01-01

    The purpose of this Analysis and Modeling Report (AMR) is to validate the External Accumulation Model that predicts accumulation of fissile materials in fractures and lithophysae in the rock beneath a degrading waste package (WP) in the potential monitored geologic repository at Yucca Mountain. (Lithophysae are voids in the rock having concentric shells of finely crystalline alkali feldspar, quartz, and other materials that were formed due to entrapped gas that later escaped, DOE 1998, p. A-25.) The intended use of this model is to estimate the quantities of external accumulation of fissile material for use in external criticality risk assessments for different types of degrading WPs: U.S. Department of Energy (DOE) Spent Nuclear Fuel (SNF) codisposed with High Level Waste (HLW) glass, commercial SNF, and Immobilized Plutonium Ceramic (Pu-ceramic) codisposed with HLW glass. The scope of the model validation is to (1) describe the model and the parameters used to develop the model, (2) provide rationale for selection of the parameters by comparisons with measured values, and (3) demonstrate that the parameters chosen are the most conservative selection for external criticality risk calculations. To demonstrate the applicability of the model, a Pu-ceramic WP is used as an example. The model begins with a source term from separately documented EQ6 calculations; where the source term is defined as the composition versus time of the water flowing out of a breached waste package (WP). Next, PHREEQC, is used to simulate the transport and interaction of the source term with the resident water and fractured tuff below the repository. In these simulations the primary mechanism for accumulation is mixing of the high pH, actinide-laden source term with resident water; thus lowering the pH values sufficiently for fissile minerals to become insoluble and precipitate. In the final section of the model, the outputs from PHREEQC, are processed to produce mass of accumulation

  11. Verifying and Validating Simulation Models

    Energy Technology Data Exchange (ETDEWEB)

    Hemez, Francois M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-23

    This presentation is a high-level discussion of the Verification and Validation (V&V) of computational models. Definitions of V&V are given to emphasize that “validation” is never performed in a vacuum; it accounts, instead, for the current state-of-knowledge in the discipline considered. In particular comparisons between physical measurements and numerical predictions should account for their respective sources of uncertainty. The differences between error (bias), aleatoric uncertainty (randomness) and epistemic uncertainty (ignorance, lack-of- knowledge) are briefly discussed. Four types of uncertainty in physics and engineering are discussed: 1) experimental variability, 2) variability and randomness, 3) numerical uncertainty and 4) model-form uncertainty. Statistical sampling methods are available to propagate, and analyze, variability and randomness. Numerical uncertainty originates from the truncation error introduced by the discretization of partial differential equations in time and space. Model-form uncertainty is introduced by assumptions often formulated to render a complex problem more tractable and amenable to modeling and simulation. The discussion concludes with high-level guidance to assess the “credibility” of numerical simulations, which stems from the level of rigor with which these various sources of uncertainty are assessed and quantified.

  12. A closed-form analytical model for predicting 3D boundary layer displacement thickness for the validation of viscous flow solvers

    Science.gov (United States)

    Kumar, V. R. Sanal; Sankar, Vigneshwaran; Chandrasekaran, Nichith; Saravanan, Vignesh; Natarajan, Vishnu; Padmanabhan, Sathyan; Sukumaran, Ajith; Mani, Sivabalan; Rameshkumar, Tharikaa; Nagaraju Doddi, Hema Sai; Vysaprasad, Krithika; Sharan, Sharad; Murugesh, Pavithra; Shankar, S. Ganesh; Nejaamtheen, Mohammed Niyasdeen; Baskaran, Roshan Vignesh; Rahman Mohamed Rafic, Sulthan Ariff; Harisrinivasan, Ukeshkumar; Srinivasan, Vivek

    2018-02-01

    A closed-form analytical model is developed for estimating the 3D boundary-layer-displacement thickness of an internal flow system at the Sanal flow choking condition for adiabatic flows obeying the physics of compressible viscous fluids. At this unique condition the boundary-layer blockage induced fluid-throat choking and the adiabatic wall-friction persuaded flow choking occur at a single sonic-fluid-throat location. The beauty and novelty of this model is that without missing the flow physics we could predict the exact boundary-layer blockage of both 2D and 3D cases at the sonic-fluid-throat from the known values of the inlet Mach number, the adiabatic index of the gas and the inlet port diameter of the internal flow system. We found that the 3D blockage factor is 47.33 % lower than the 2D blockage factor with air as the working fluid. We concluded that the exact prediction of the boundary-layer-displacement thickness at the sonic-fluid-throat provides a means to correctly pinpoint the causes of errors of the viscous flow solvers. The methodology presented herein with state-of-the-art will play pivotal roles in future physical and biological sciences for a credible verification, calibration and validation of various viscous flow solvers for high-fidelity 2D/3D numerical simulations of real-world flows. Furthermore, our closed-form analytical model will be useful for the solid and hybrid rocket designers for the grain-port-geometry optimization of new generation single-stage-to-orbit dual-thrust-motors with the highest promising propellant loading density within the given envelope without manifestation of the Sanal flow choking leading to possible shock waves causing catastrophic failures.

  13. A closed-form analytical model for predicting 3D boundary layer displacement thickness for the validation of viscous flow solvers

    Directory of Open Access Journals (Sweden)

    V. R. Sanal Kumar

    2018-02-01

    Full Text Available A closed-form analytical model is developed for estimating the 3D boundary-layer-displacement thickness of an internal flow system at the Sanal flow choking condition for adiabatic flows obeying the physics of compressible viscous fluids. At this unique condition the boundary-layer blockage induced fluid-throat choking and the adiabatic wall-friction persuaded flow choking occur at a single sonic-fluid-throat location. The beauty and novelty of this model is that without missing the flow physics we could predict the exact boundary-layer blockage of both 2D and 3D cases at the sonic-fluid-throat from the known values of the inlet Mach number, the adiabatic index of the gas and the inlet port diameter of the internal flow system. We found that the 3D blockage factor is 47.33 % lower than the 2D blockage factor with air as the working fluid. We concluded that the exact prediction of the boundary-layer-displacement thickness at the sonic-fluid-throat provides a means to correctly pinpoint the causes of errors of the viscous flow solvers. The methodology presented herein with state-of-the-art will play pivotal roles in future physical and biological sciences for a credible verification, calibration and validation of various viscous flow solvers for high-fidelity 2D/3D numerical simulations of real-world flows. Furthermore, our closed-form analytical model will be useful for the solid and hybrid rocket designers for the grain-port-geometry optimization of new generation single-stage-to-orbit dual-thrust-motors with the highest promising propellant loading density within the given envelope without manifestation of the Sanal flow choking leading to possible shock waves causing catastrophic failures.

  14. Modelling bankruptcy prediction models in Slovak companies

    Directory of Open Access Journals (Sweden)

    Kovacova Maria

    2017-01-01

    Full Text Available An intensive research from academics and practitioners has been provided regarding models for bankruptcy prediction and credit risk management. In spite of numerous researches focusing on forecasting bankruptcy using traditional statistics techniques (e.g. discriminant analysis and logistic regression and early artificial intelligence models (e.g. artificial neural networks, there is a trend for transition to machine learning models (support vector machines, bagging, boosting, and random forest to predict bankruptcy one year prior to the event. Comparing the performance of this with unconventional approach with results obtained by discriminant analysis, logistic regression, and neural networks application, it has been found that bagging, boosting, and random forest models outperform the others techniques, and that all prediction accuracy in the testing sample improves when the additional variables are included. On the other side the prediction accuracy of old and well known bankruptcy prediction models is quiet high. Therefore, we aim to analyse these in some way old models on the dataset of Slovak companies to validate their prediction ability in specific conditions. Furthermore, these models will be modelled according to new trends by calculating the influence of elimination of selected variables on the overall prediction ability of these models.

  15. Validating the passenger traffic model for Copenhagen

    DEFF Research Database (Denmark)

    Overgård, Christian Hansen; VUK, Goran

    2006-01-01

    The paper presents a comprehensive validation procedure for the passenger traffic model for Copenhagen based on external data from the Danish national travel survey and traffic counts. The model was validated for the years 2000 to 2004, with 2004 being of particular interest because the Copenhagen...... matched the observed traffic better than those of the transit assignment model. With respect to the metro forecasts, the model over-predicts metro passenger flows by 10% to 50%. The wide range of findings from the project resulted in two actions. First, a project was started in January 2005 to upgrade...

  16. Biomarker case-detection and prediction with potential for functional psychosis screening: development and validation of a model related to biochemistry, sensory neural timing and end organ performance.

    Directory of Open Access Journals (Sweden)

    Stephanie eFryar-Williams

    2016-04-01

    Full Text Available The Mental Health Biomarker Project aimed to discover case-predictive biomarkers for functional psychosis. In a retrospective, cross-sectional study, candidate marker results from 67, highly-characterized symptomatic participants were compared with results from 67 gender and age matched controls. Urine samples were analysed for catecholamines, their metabolites and hydroxylpyrolline-2-one, an oxidative stress marker. Blood samples were analyzed for vitamin and trace element cofactors of enzymes in the catecholamine synthesis and metabolism pathways. Cognitive, auditory and visual processing measures were assessed using a simple 45 minute, office-based procedure. Receiver Operating Curve (ROC and Odds Ratio analysis discovered biomarkers for deficits in folate, vitamin D and B6 and elevations in free copper to zinc ratio, catecholamines and the oxidative stress marker. Deficits were discovered in peripheral visual and auditory end-organ function, intra-cerebral auditory and visual processing speed and dichotic-listening performance. 15 ROC biomarker variables were divided into 5 functional domains. Through a repeated ROC process, individual ROC variables, followed by domains and finally the overall 15 set model, were dichotomously scored and tallied for abnormal results upon which it was found that ≥ 3 out of 5 abnormal domains achieved an AUC of 0.952 with a sensitivity of 84 per cent and a specificity of 90 percent. Six additional middle ear biomarkers in a 21 biomarker set increased sensitivity to 94% percent. Fivefold cross-validation yielded a mean sensitivity of 85% for the 15 biomarker set. Non-parametric regression analysis confirmed that ≥ 3 out of 5 abnormally scored domains predicted > 50% risk of case-ness whilst 4 abnormally-scored domains predicted 88% risk of case-ness and 100% diagnostic certainty was reached when all 5 domains were abnormally scored. These findings require validation in prospective cohorts and other mental

  17. Archaeological predictive model set.

    Science.gov (United States)

    2015-03-01

    This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

  18. CFD Validation Studies for Hypersonic Flow Prediction

    Science.gov (United States)

    Gnoffo, Peter A.

    2001-01-01

    A series of experiments to measure pressure and heating for code validation involving hypersonic, laminar, separated flows was conducted at the Calspan-University at Buffalo Research Center (CUBRC) in the Large Energy National Shock (LENS) tunnel. The experimental data serves as a focus for a code validation session but are not available to the authors until the conclusion of this session. The first set of experiments considered here involve Mach 9.5 and Mach 11.3 N2 flow over a hollow cylinder-flare with 30 degree flare angle at several Reynolds numbers sustaining laminar, separated flow. Truncated and extended flare configurations are considered. The second set of experiments, at similar conditions, involves flow over a sharp, double cone with fore-cone angle of 25 degrees and aft-cone angle of 55 degrees. Both sets of experiments involve 30 degree compressions. Location of the separation point in the numerical simulation is extremely sensitive to the level of grid refinement in the numerical predictions. The numerical simulations also show a significant influence of Reynolds number on extent of separation. Flow unsteadiness was easily introduced into the double cone simulations using aggressive relaxation parameters that normally promote convergence.

  19. Wind power prediction models

    Science.gov (United States)

    Levy, R.; Mcginness, H.

    1976-01-01

    Investigations were performed to predict the power available from the wind at the Goldstone, California, antenna site complex. The background for power prediction was derived from a statistical evaluation of available wind speed data records at this location and at nearby locations similarly situated within the Mojave desert. In addition to a model for power prediction over relatively long periods of time, an interim simulation model that produces sample wind speeds is described. The interim model furnishes uncorrelated sample speeds at hourly intervals that reproduce the statistical wind distribution at Goldstone. A stochastic simulation model to provide speed samples representative of both the statistical speed distributions and correlations is also discussed.

  20. A discussion on validation of hydrogeological models

    International Nuclear Information System (INIS)

    Carrera, J.; Mousavi, S.F.; Usunoff, E.J.; Sanchez-Vila, X.; Galarza, G.

    1993-01-01

    Groundwater flow and solute transport are often driven by heterogeneities that elude easy identification. It is also difficult to select and describe the physico-chemical processes controlling solute behaviour. As a result, definition of a conceptual model involves numerous assumptions both on the selection of processes and on the representation of their spatial variability. Validating a numerical model by comparing its predictions with actual measurements may not be sufficient for evaluating whether or not it provides a good representation of 'reality'. Predictions will be close to measurements, regardless of model validity, if these are taken from experiments that stress well-calibrated model modes. On the other hand, predictions will be far from measurements when model parameters are very uncertain, even if the model is indeed a very good representation of the real system. Hence, we contend that 'classical' validation of hydrogeological models is not possible. Rather, models should be viewed as theories about the real system. We propose to follow a rigorous modeling approach in which different sources of uncertainty are explicitly recognized. The application of one such approach is illustrated by modeling a laboratory uranium tracer test performed on fresh granite, which was used as Test Case 1b in INTRAVAL. (author)

  1. Validating Dart Model

    Directory of Open Access Journals (Sweden)

    Mazur Jolanta

    2014-12-01

    Full Text Available The primary objective of the study was to quantitatively test the DART model, which despite being one of the most popular representations of co-creation concept was so far studied almost solely with qualitative methods. To this end, the researchers developed a multiple measurement scale and employed it in interviewing managers. The statistical evidence for adequacy of the model was obtained through CFA with AMOS software. The findings suggest that the DART model may not be an accurate representation of co-creation practices in companies. From the data analysis it was evident that the building blocks of DART had too much of conceptual overlap to be an effective framework for quantitative analysis. It was also implied that the phenomenon of co-creation is so rich and multifaceted that it may be more adequately captured by a measurement model where co-creation is conceived as a third-level factor with two layers of intermediate latent variables.

  2. Gene prediction validation and functional analysis of redundant pathways

    DEFF Research Database (Denmark)

    Sønderkær, Mads

    2011-01-01

    have employed a large mRNA-seq data set to improve and validate ab initio predicted gene models. This direct experimental evidence also provides reliable determinations of UTR regions and polyadenylation sites, which are not easily predicted in plants. Furthermore, once an annotated genome sequence...... is available, gene expression by mRNA-Seq enables acquisition of a more complete overview of gene isoform usage in complex enzymatic pathways enabling the identification of key genes. Metabolism in potatoes This information is useful e.g. for crop improvement based on manipulation of agronomically important...

  3. Prediction models : the right tool for the right problem

    NARCIS (Netherlands)

    Kappen, Teus H.; Peelen, Linda M.

    2016-01-01

    PURPOSE OF REVIEW: Perioperative prediction models can help to improve personalized patient care by providing individual risk predictions to both patients and providers. However, the scientific literature on prediction model development and validation can be quite technical and challenging to

  4. Validation of the STAFF-5 computer model

    International Nuclear Information System (INIS)

    Fletcher, J.F.; Fields, S.R.

    1981-04-01

    STAFF-5 is a dynamic heat-transfer-fluid-flow stress model designed for computerized prediction of the temperature-stress performance of spent LWR fuel assemblies under storage/disposal conditions. Validation of the temperature calculating abilities of this model was performed by comparing temperature calculations under specified conditions to experimental data from the Engine Maintenance and Dissassembly (EMAD) Fuel Temperature Test Facility and to calculations performed by Battelle Pacific Northwest Laboratory (PNL) using the HYDRA-1 model. The comparisons confirmed the ability of STAFF-5 to calculate representative fuel temperatures over a considerable range of conditions, as a first step in the evaluation and prediction of fuel temperature-stress performance

  5. Perpetual Model Validation

    Science.gov (United States)

    2017-03-01

    25]. This inference process is carried out by a tool referred to as Hynger (Hybrid iNvariant GEneratoR), overviewed in Figure 4, which is a MATLAB ...initially on memory access patterns. A monitoring module will check, at runtime that the observed memory access pattern matches the pattern the software is...necessary. By using the developed approach, a model may be derived from initial tests or simulations , which will then be formally checked at runtime

  6. Validation of models using Chernobyl fallout data from the Central Bohemia region of the Czech Republic. Scenario CB. First report of the VAMP Multiple Pathways Assessment Working Group. Part of the IAEA/CEC Co-ordinated Research Programme on the Validation of Environmental Model Predictions (VAMP)

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-04-01

    The VAMP Multiple Pathways Assessment Working Group is an international forum for the testing and comparison of model predictions. The emphasis is on evaluating transfer from the environment to human via all pathways which are relevant in the environment being considered. This document is the first report of the Group and contains the results of the first test exercise on the validation of multiple pathways assessment models using Chernobyl fallout data obtained from the Central Bohemia (CB) region of the Czech Republic (Scenario CB). The report includes the following three appendixes: Documentation and evaluation of model validation data used in scenario CB (3 papers), Description of models used in scenario CB (1 paper), Individual evaluations of model predictions for scenario CB (13 papers). A separate abstract was prepared for each paper. Refs, figs and tabs.

  7. Validation of models using Chernobyl fallout data from the Central Bohemia region of the Czech Republic. Scenario CB. First report of the VAMP Multiple Pathways Assessment Working Group. Part of the IAEA/CEC Co-ordinated Research Programme on the Validation of Environmental Model Predictions (VAMP)

    International Nuclear Information System (INIS)

    1995-04-01

    The VAMP Multiple Pathways Assessment Working Group is an international forum for the testing and comparison of model predictions. The emphasis is on evaluating transfer from the environment to human via all pathways which are relevant in the environment being considered. This document is the first report of the Group and contains the results of the first test exercise on the validation of multiple pathways assessment models using Chernobyl fallout data obtained from the Central Bohemia (CB) region of the Czech Republic (Scenario CB). The report includes the following three appendixes: Documentation and evaluation of model validation data used in scenario CB (3 papers), Description of models used in scenario CB (1 paper), Individual evaluations of model predictions for scenario CB (13 papers). A separate abstract was prepared for each paper. Refs, figs and tabs

  8. Inverse and Predictive Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Syracuse, Ellen Marie [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-09-27

    The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an even greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.

  9. Validation of a prediction model that allows direct comparison of the Oxford Knee Score and American Knee Society clinical rating system.

    Science.gov (United States)

    Maempel, J F; Clement, N D; Brenkel, I J; Walmsley, P J

    2015-04-01

    This study demonstrates a significant correlation between the American Knee Society (AKS) Clinical Rating System and the Oxford Knee Score (OKS) and provides a validated prediction tool to estimate score conversion. A total of 1022 patients were prospectively clinically assessed five years after TKR and completed AKS assessments and an OKS questionnaire. Multivariate regression analysis demonstrated significant correlations between OKS and the AKS knee and function scores but a stronger correlation (r = 0.68, p Society of Bone & Joint Surgery.

  10. Validating Inertial Confinement Fusion (ICF) predictive capability using perturbed capsules

    Science.gov (United States)

    Schmitt, Mark; Magelssen, Glenn; Tregillis, Ian; Hsu, Scott; Bradley, Paul; Dodd, Evan; Cobble, James; Flippo, Kirk; Offerman, Dustin; Obrey, Kimberly; Wang, Yi-Ming; Watt, Robert; Wilke, Mark; Wysocki, Frederick; Batha, Steven

    2009-11-01

    Achieving ignition on NIF is a monumental step on the path toward utilizing fusion as a controlled energy source. Obtaining robust ignition requires accurate ICF models to predict the degradation of ignition caused by heterogeneities in capsule construction and irradiation. LANL has embarked on a project to induce controlled defects in capsules to validate our ability to predict their effects on fusion burn. These efforts include the validation of feature-driven hydrodynamics and mix in a convergent geometry. This capability is needed to determine the performance of capsules imploded under less-than-optimum conditions on future IFE facilities. LANL's recently initiated Defect Implosion Experiments (DIME) conducted at Rochester's Omega facility are providing input for these efforts. Recent simulation and experimental results will be shown.

  11. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites

    Czech Academy of Sciences Publication Activity Database

    Zhou, Y.; Wu, X.; Weiming, J.; Chen, J.; Wang, S.; Wang, H.; Wenping, Y.; Black, T. A.; Jassal, R.; Ibrom, A.; Han, S.; Yan, J.; Margolis, H.; Roupsard, O.; Li, Y.; Zhao, F.; Kiely, G.; Starr, G.; Pavelka, Marian; Montagnani, L.; Wohlfahrt, G.; D'Odorico, P.; Cook, D.; Altaf Arain, M.; Bonal, D.; Beringer, J.; Blanken, P. D.; Loubet, B.; Leclerc, M. Y.; Matteucci, G.; Nagy, Z.; Olejnik, Janusz; U., K. T. P.; Varlagin, A.

    2016-01-01

    Roč. 36, č. 7 (2016), s. 2743-2760 ISSN 2169-8953 Institutional support: RVO:67179843 Keywords : global parametrization * predicting model * FlUXNET Subject RIV: EH - Ecology, Behaviour Impact factor: 3.395, year: 2016

  12. Calibration and validation of an activated sludge model for greenhouse gases no. 1 (ASMG1): prediction of temperature-dependent N₂O emission dynamics.

    Science.gov (United States)

    Guo, Lisha; Vanrolleghem, Peter A

    2014-02-01

    An activated sludge model for greenhouse gases no. 1 was calibrated with data from a wastewater treatment plant (WWTP) without control systems and validated with data from three similar plants equipped with control systems. Special about the calibration/validation approach adopted in this paper is that the data are obtained from simulations with a mathematical model that is widely accepted to describe effluent quality and operating costs of actual WWTPs, the Benchmark Simulation Model No. 2 (BSM2). The calibration also aimed at fitting the model to typical observed nitrous oxide (N₂O) emission data, i.e., a yearly average of 0.5% of the influent total nitrogen load emitted as N₂O-N. Model validation was performed by challenging the model in configurations with different control strategies. The kinetic term describing the dissolved oxygen effect on the denitrification by ammonia-oxidizing bacteria (AOB) was modified into a Haldane term. Both original and Haldane-modified models passed calibration and validation. Even though their yearly averaged values were similar, the two models presented different dynamic N₂O emissions under cold temperature conditions and control. Therefore, data collected in such situations can potentially permit model discrimination. Observed seasonal trends in N₂O emissions are simulated well with both original and Haldane-modified models. A mechanistic explanation based on the temperature-dependent interaction between heterotrophic and autotrophic N₂O pathways was provided. Finally, while adding the AOB denitrification pathway to a model with only heterotrophic N₂O production showed little impact on effluent quality and operating cost criteria, it clearly affected N2O emission productions.

  13. EFFICIENT PREDICTIVE MODELLING FOR ARCHAEOLOGICAL RESEARCH

    OpenAIRE

    Balla, A.; Pavlogeorgatos, G.; Tsiafakis, D.; Pavlidis, G.

    2014-01-01

    The study presents a general methodology for designing, developing and implementing predictive modelling for identifying areas of archaeological interest. The methodology is based on documented archaeological data and geographical factors, geospatial analysis and predictive modelling, and has been applied to the identification of possible Macedonian tombs’ locations in Northern Greece. The model was tested extensively and the results were validated using a commonly used predictive gain, which...

  14. Establishment and validation of a predictive nomogram model for non-small cell lung cancer patients with chronic hepatitis B viral infection.

    Science.gov (United States)

    Chen, Shulin; Lai, Yanzhen; He, Zhengqiang; Li, Jianpei; He, Xia; Shen, Rui; Ding, Qiuying; Chen, Hao; Peng, Songguo; Liu, Wanli

    2018-05-04

    This study aimed to establish an effective predictive nomogram for non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. The nomogram was based on a retrospective study of 230 NSCLC patients with chronic HBV infection. The predictive accuracy and discriminative ability of the nomogram were determined by a concordance index (C-index), calibration plot and decision curve analysis and were compared with the current tumor, node, and metastasis (TNM) staging system. Independent factors derived from Kaplan-Meier analysis of the primary cohort to predict overall survival (OS) were all assembled into a Cox proportional hazards regression model to build the nomogram model. The final model included age, tumor size, TNM stage, treatment, apolipoprotein A-I, apolipoprotein B, glutamyl transpeptidase and lactate dehydrogenase. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with the actual observations. The C-index of the model for predicting OS had a superior discrimination power compared with the TNM staging system [0.780 (95% CI 0.733-0.827) vs. 0.693 (95% CI 0.640-0.746), P  20.0). The proposed nomogram model resulted in more accurate prognostic prediction for NSCLC patients with chronic HBV infection.

  15. Structural system identification: Structural dynamics model validation

    Energy Technology Data Exchange (ETDEWEB)

    Red-Horse, J.R.

    1997-04-01

    Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation.

  16. External validation of EPIWIN biodegradation models.

    Science.gov (United States)

    Posthumus, R; Traas, T P; Peijnenburg, W J G M; Hulzebos, E M

    2005-01-01

    The BIOWIN biodegradation models were evaluated for their suitability for regulatory purposes. BIOWIN includes the linear and non-linear BIODEG and MITI models for estimating the probability of rapid aerobic biodegradation and an expert survey model for primary and ultimate biodegradation estimation. Experimental biodegradation data for 110 newly notified substances were compared with the estimations of the different models. The models were applied separately and in combinations to determine which model(s) showed the best performance. The results of this study were compared with the results of other validation studies and other biodegradation models. The BIOWIN models predict not-readily biodegradable substances with high accuracy in contrast to ready biodegradability. In view of the high environmental concern of persistent chemicals and in view of the large number of not-readily biodegradable chemicals compared to the readily ones, a model is preferred that gives a minimum of false positives without a corresponding high percentage false negatives. A combination of the BIOWIN models (BIOWIN2 or BIOWIN6) showed the highest predictive value for not-readily biodegradability. However, the highest score for overall predictivity with lowest percentage false predictions was achieved by applying BIOWIN3 (pass level 2.75) and BIOWIN6.

  17. Evaluating the Predictive Validity of Graduate Management Admission Test Scores

    Science.gov (United States)

    Sireci, Stephen G.; Talento-Miller, Eileen

    2006-01-01

    Admissions data and first-year grade point average (GPA) data from 11 graduate management schools were analyzed to evaluate the predictive validity of Graduate Management Admission Test[R] (GMAT[R]) scores and the extent to which predictive validity held across sex and race/ethnicity. The results indicated GMAT verbal and quantitative scores had…

  18. The Predictive Validity of Teacher Candidate Letters of Reference

    Science.gov (United States)

    Mason, Richard W.; Schroeder, Mark P.

    2014-01-01

    Letters of reference are widely used as an essential part of the hiring process of newly licensed teachers. While the predictive validity of these letters of reference has been called into question it has never been empirically studied. The current study examined the predictive validity of the quality of letters of reference for forty-one student…

  19. Validation process of simulation model

    International Nuclear Information System (INIS)

    San Isidro, M. J.

    1998-01-01

    It is presented a methodology on empirical validation about any detailed simulation model. This king of validation it is always related with an experimental case. The empirical validation has a residual sense, because the conclusions are based on comparisons between simulated outputs and experimental measurements. This methodology will guide us to detect the fails of the simulation model. Furthermore, it can be used a guide in the design of posterior experiments. Three steps can be well differentiated: Sensitivity analysis. It can be made with a DSA, differential sensitivity analysis, and with a MCSA, Monte-Carlo sensitivity analysis. Looking the optimal domains of the input parameters. It has been developed a procedure based on the Monte-Carlo methods and Cluster techniques, to find the optimal domains of these parameters. Residual analysis. This analysis has been made on the time domain and on the frequency domain, it has been used the correlation analysis and spectral analysis. As application of this methodology, it is presented the validation carried out on a thermal simulation model on buildings, Esp., studying the behavior of building components on a Test Cell of LECE of CIEMAT. (Author) 17 refs

  20. Predicting risk behaviors: development and validation of a diagnostic scale.

    Science.gov (United States)

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  1. A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

    Directory of Open Access Journals (Sweden)

    Sergio Briguglio

    2003-01-01

    Full Text Available A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage and OpenMP (at the intra-node one. The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

  2. Prediction of skull fracture risk for children 0-9 months old through validated parametric finite element model and cadaver test reconstruction.

    Science.gov (United States)

    Li, Zhigang; Liu, Weiguo; Zhang, Jinhuan; Hu, Jingwen

    2015-09-01

    Skull fracture is one of the most common pediatric traumas. However, injury assessment tools for predicting pediatric skull fracture risk is not well established mainly due to the lack of cadaver tests. Weber conducted 50 pediatric cadaver drop tests for forensic research on child abuse in the mid-1980s (Experimental studies of skull fractures in infants, Z Rechtsmed. 92: 87-94, 1984; Biomechanical fragility of the infant skull, Z Rechtsmed. 94: 93-101, 1985). To our knowledge, these studies contained the largest sample size among pediatric cadaver tests in the literature. However, the lack of injury measurements limited their direct application in investigating pediatric skull fracture risks. In this study, 50 pediatric cadaver tests from Weber's studies were reconstructed using a parametric pediatric head finite element (FE) model which were morphed into subjects with ages, head sizes/shapes, and skull thickness values that reported in the tests. The skull fracture risk curves for infants from 0 to 9 months old were developed based on the model-predicted head injury measures through logistic regression analysis. It was found that the model-predicted stress responses in the skull (maximal von Mises stress, maximal shear stress, and maximal first principal stress) were better predictors than global kinematic-based injury measures (peak head acceleration and head injury criterion (HIC)) in predicting pediatric skull fracture. This study demonstrated the feasibility of using age- and size/shape-appropriate head FE models to predict pediatric head injuries. Such models can account for the morphological variations among the subjects, which cannot be considered by a single FE human model.

  3. Prediction model for recurrence probabilities after intravesical chemotherapy in patients with intermediate-risk non-muscle-invasive bladder cancer, including external validation

    NARCIS (Netherlands)

    Lammers, R.J.M.; Hendriks, J.C.M.; Rodriguez Faba, O.; Witjes, W.P.J.; Palou, J.; Witjes, J.A.

    2016-01-01

    PURPOSE: To develop a model to predict recurrence for patients with intermediate-risk (IR) non-muscle-invasive bladder cancer (NMIBC) treated with intravesical chemotherapy which can be challenging because of the heterogeneous characteristics of these patients. METHODS: Data from three Dutch trials

  4. Verification and validation of models

    International Nuclear Information System (INIS)

    Herbert, A.W.; Hodgkinson, D.P.; Jackson, C.P.; Lever, D.A.; Robinson, P.C.

    1986-12-01

    The numerical accuracy of the computer models for groundwater flow and radionuclide transport that are to be used in repository safety assessment must be tested, and their ability to describe experimental data assessed: they must be verified and validated respectively. Also appropriate ways to use the codes in performance assessments, taking into account uncertainties in present data and future conditions, must be studied. These objectives are being met by participation in international exercises, by developing bench-mark problems, and by analysing experiments. In particular the project has funded participation in the HYDROCOIN project for groundwater flow models, the Natural Analogues Working Group, and the INTRAVAL project for geosphere models. (author)

  5. Prediction and validation of diffusion coefficients in a model drug delivery system using microsecond atomistic molecular dynamics simulation and vapour sorption analysis.

    Science.gov (United States)

    Forrey, Christopher; Saylor, David M; Silverstein, Joshua S; Douglas, Jack F; Davis, Eric M; Elabd, Yossef A

    2014-10-14

    Diffusion of small to medium sized molecules in polymeric medical device materials underlies a broad range of public health concerns related to unintended leaching from or uptake into implantable medical devices. However, obtaining accurate diffusion coefficients for such systems at physiological temperature represents a formidable challenge, both experimentally and computationally. While molecular dynamics simulation has been used to accurately predict the diffusion coefficients, D, of a handful of gases in various polymers, this success has not been extended to molecules larger than gases, e.g., condensable vapours, liquids, and drugs. We present atomistic molecular dynamics simulation predictions of diffusion in a model drug eluting system that represent a dramatic improvement in accuracy compared to previous simulation predictions for comparable systems. We find that, for simulations of insufficient duration, sub-diffusive dynamics can lead to dramatic over-prediction of D. We present useful metrics for monitoring the extent of sub-diffusive dynamics and explore how these metrics correlate to error in D. We also identify a relationship between diffusion and fast dynamics in our system, which may serve as a means to more rapidly predict diffusion in slowly diffusing systems. Our work provides important precedent and essential insights for utilizing atomistic molecular dynamics simulations to predict diffusion coefficients of small to medium sized molecules in condensed soft matter systems.

  6. Validation of ecological state space models using the Laplace approximation

    DEFF Research Database (Denmark)

    Thygesen, Uffe Høgsbro; Albertsen, Christoffer Moesgaard; Berg, Casper Willestofte

    2017-01-01

    Many statistical models in ecology follow the state space paradigm. For such models, the important step of model validation rarely receives as much attention as estimation or hypothesis testing, perhaps due to lack of available algorithms and software. Model validation is often based on a naive...... for estimation in general mixed effects models. Implementing one-step predictions in the R package Template Model Builder, we demonstrate that it is possible to perform model validation with little effort, even if the ecological model is multivariate, has non-linear dynamics, and whether observations...... useful directions in which the model could be improved....

  7. SPR Hydrostatic Column Model Verification and Validation.

    Energy Technology Data Exchange (ETDEWEB)

    Bettin, Giorgia [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lord, David [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rudeen, David Keith [Gram, Inc. Albuquerque, NM (United States)

    2015-10-01

    A Hydrostatic Column Model (HCM) was developed to help differentiate between normal "tight" well behavior and small-leak behavior under nitrogen for testing the pressure integrity of crude oil storage wells at the U.S. Strategic Petroleum Reserve. This effort was motivated by steady, yet distinct, pressure behavior of a series of Big Hill caverns that have been placed under nitrogen for extended period of time. This report describes the HCM model, its functional requirements, the model structure and the verification and validation process. Different modes of operation are also described, which illustrate how the software can be used to model extended nitrogen monitoring and Mechanical Integrity Tests by predicting wellhead pressures along with nitrogen interface movements. Model verification has shown that the program runs correctly and it is implemented as intended. The cavern BH101 long term nitrogen test was used to validate the model which showed very good agreement with measured data. This supports the claim that the model is, in fact, capturing the relevant physical phenomena and can be used to make accurate predictions of both wellhead pressure and interface movements.

  8. Cultural Resource Predictive Modeling

    Science.gov (United States)

    2017-10-01

    CR cultural resource CRM cultural resource management CRPM Cultural Resource Predictive Modeling DoD Department of Defense ESTCP Environmental...resource management ( CRM ) legal obligations under NEPA and the NHPA, military installations need to demonstrate that CRM decisions are based on objective...maxim “one size does not fit all,” and demonstrate that DoD installations have many different CRM needs that can and should be met through a variety

  9. Reliable prediction of clinical outcome in patients with chronic HCV infection and compensated advanced hepatic fibrosis: a validated model using objective and readily available clinical parameters.

    Science.gov (United States)

    van der Meer, Adriaan J; Hansen, Bettina E; Fattovich, Giovanna; Feld, Jordan J; Wedemeyer, Heiner; Dufour, Jean-François; Lammert, Frank; Duarte-Rojo, Andres; Manns, Michael P; Ieluzzi, Donatella; Zeuzem, Stefan; Hofmann, W Peter; de Knegt, Robert J; Veldt, Bart J; Janssen, Harry L A

    2015-02-01

    Reliable tools to predict long-term outcome among patients with well compensated advanced liver disease due to chronic HCV infection are lacking. Risk scores for mortality and for cirrhosis-related complications were constructed with Cox regression analysis in a derivation cohort and evaluated in a validation cohort, both including patients with chronic HCV infection and advanced fibrosis. In the derivation cohort, 100/405 patients died during a median 8.1 (IQR 5.7-11.1) years of follow-up. Multivariate Cox analyses showed age (HR=1.06, 95% CI 1.04 to 1.09, pstatistic=0.78, 95% CI 0.72 to 0.83). In the validation cohort, 58/296 patients with cirrhosis died during a median of 6.6 (IQR 4.4-9.0) years. Among patients with estimated 5-year mortality risks 10%, the observed 5-year mortality rates in the derivation cohort and validation cohort were 0.9% (95% CI 0.0 to 2.7) and 2.6% (95% CI 0.0 to 6.1), 8.1% (95% CI 1.8 to 14.4) and 8.0% (95% CI 1.3 to 14.7), 21.8% (95% CI 13.2 to 30.4) and 20.9% (95% CI 13.6 to 28.1), respectively (C statistic in validation cohort = 0.76, 95% CI 0.69 to 0.83). The risk score for cirrhosis-related complications also incorporated HCV genotype (C statistic = 0.80, 95% CI 0.76 to 0.83 in the derivation cohort; and 0.74, 95% CI 0.68 to 0.79 in the validation cohort). Prognosis of patients with chronic HCV infection and compensated advanced liver disease can be accurately assessed with risk scores including readily available objective clinical parameters. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  10. Assessing the radiological impact of past nuclear activities and events. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    1994-07-01

    The report is a compilation of papers presented during the July 1993 Special Plenary Session of the VAMP (Validation of Environmental Model Predictions). The papers are grouped in 4 chapters: Assessment in the vicinity of nuclear weapons test sites (4 papers), Assessment in the vicinity of nuclear weapons production facilities (2 papers), Post-Chernobyl dose assessment studies (4 papers) and Assessment in the vicinity of dumped radioactive waste (1 paper). A separate abstract was prepared for each paper. Refs, figs and tabs

  11. Extracting falsifiable predictions from sloppy models.

    Science.gov (United States)

    Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

    2007-12-01

    Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

  12. Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325).

    Science.gov (United States)

    Dehing-Oberije, Cary; Aerts, Hugo; Yu, Shipeng; De Ruysscher, Dirk; Menheere, Paul; Hilvo, Mika; van der Weide, Hiska; Rao, Bharat; Lambin, Philippe

    2011-10-01

    Currently, prediction of survival for non-small-cell lung cancer patients treated with (chemo)radiotherapy is mainly based on clinical factors. The hypothesis of this prospective study was that blood biomarkers related to hypoxia, inflammation, and tumor load would have an added prognostic value for predicting survival. Clinical data and blood samples were collected prospectively (NCT00181519, NCT00573040, and NCT00572325) from 106 inoperable non-small-cell lung cancer patients (Stages I-IIIB), treated with curative intent with radiotherapy alone or combined with chemotherapy. Blood biomarkers, including lactate dehydrogenase, C-reactive protein, osteopontin, carbonic anhydrase IX, interleukin (IL) 6, IL-8, carcinoembryonic antigen (CEA), and cytokeratin fragment 21-1, were measured. A multivariate model, built on a large patient population (N = 322) and externally validated, was used as a baseline model. An extended model was created by selecting additional biomarkers. The model's performance was expressed as the area under the curve (AUC) of the receiver operating characteristic and assessed by use of leave-one-out cross validation as well as a validation cohort (n = 52). The baseline model consisted of gender, World Health Organization performance status, forced expiratory volume, number of positive lymph node stations, and gross tumor volume and yielded an AUC of 0.72. The extended model included two additional blood biomarkers (CEA and IL-6) and resulted in a leave-one-out AUC of 0.81. The performance of the extended model was significantly better than the clinical model (p = 0.004). The AUC on the validation cohort was 0.66 and 0.76, respectively. The performance of the prognostic model for survival improved markedly by adding two blood biomarkers: CEA and IL-6. Copyright © 2011 Elsevier Inc. All rights reserved.

  13. Candidate Prediction Models and Methods

    DEFF Research Database (Denmark)

    Nielsen, Henrik Aalborg; Nielsen, Torben Skov; Madsen, Henrik

    2005-01-01

    This document lists candidate prediction models for Work Package 3 (WP3) of the PSO-project called ``Intelligent wind power prediction systems'' (FU4101). The main focus is on the models transforming numerical weather predictions into predictions of power production. The document also outlines...... the possibilities w.r.t. different numerical weather predictions actually available to the project....

  14. Polarographic validation of chemical speciation models

    International Nuclear Information System (INIS)

    Duffield, J.R.; Jarratt, J.A.

    2001-01-01

    It is well established that the chemical speciation of an element in a given matrix, or system of matrices, is of fundamental importance in controlling the transport behaviour of the element. Therefore, to accurately understand and predict the transport of elements and compounds in the environment it is a requirement that both the identities and concentrations of trace element physico-chemical forms can be ascertained. These twin requirements present the analytical scientist with considerable challenges given the labile equilibria, the range of time scales (from nanoseconds to years) and the range of concentrations (ultra-trace to macro) that may be involved. As a result of this analytical variability, chemical equilibrium modelling has become recognised as an important predictive tool in chemical speciation analysis. However, this technique requires firm underpinning by the use of complementary experimental techniques for the validation of the predictions made. The work reported here has been undertaken with the primary aim of investigating possible methodologies that can be used for the validation of chemical speciation models. However, in approaching this aim, direct chemical speciation analyses have been made in their own right. Results will be reported and analysed for the iron(II)/iron(III)-citrate proton system (pH 2 to 10; total [Fe] = 3 mmol dm -3 ; total [citrate 3- ] 10 mmol dm -3 ) in which equilibrium constants have been determined using glass electrode potentiometry, speciation is predicted using the PHREEQE computer code, and validation of predictions is achieved by determination of iron complexation and redox state with associated concentrations. (authors)

  15. PEMFC modeling and experimental validation

    Energy Technology Data Exchange (ETDEWEB)

    Vargas, J.V.C. [Federal University of Parana (UFPR), Curitiba, PR (Brazil). Dept. of Mechanical Engineering], E-mail: jvargas@demec.ufpr.br; Ordonez, J.C.; Martins, L.S. [Florida State University, Tallahassee, FL (United States). Center for Advanced Power Systems], Emails: ordonez@caps.fsu.edu, martins@caps.fsu.edu

    2009-07-01

    In this paper, a simplified and comprehensive PEMFC mathematical model introduced in previous studies is experimentally validated. Numerical results are obtained for an existing set of commercial unit PEM fuel cells. The model accounts for pressure drops in the gas channels, and for temperature gradients with respect to space in the flow direction, that are investigated by direct infrared imaging, showing that even at low current operation such gradients are present in fuel cell operation, and therefore should be considered by a PEMFC model, since large coolant flow rates are limited due to induced high pressure drops in the cooling channels. The computed polarization and power curves are directly compared to the experimentally measured ones with good qualitative and quantitative agreement. The combination of accuracy and low computational time allow for the future utilization of the model as a reliable tool for PEMFC simulation, control, design and optimization purposes. (author)

  16. Predictive Surface Complexation Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Sverjensky, Dimitri A. [Johns Hopkins Univ., Baltimore, MD (United States). Dept. of Earth and Planetary Sciences

    2016-11-29

    Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall, my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.

  17. Fingerprint verification prediction model in hand dermatitis.

    Science.gov (United States)

    Lee, Chew K; Chang, Choong C; Johor, Asmah; Othman, Puwira; Baba, Roshidah

    2015-07-01

    Hand dermatitis associated fingerprint changes is a significant problem and affects fingerprint verification processes. This study was done to develop a clinically useful prediction model for fingerprint verification in patients with hand dermatitis. A case-control study involving 100 patients with hand dermatitis. All patients verified their thumbprints against their identity card. Registered fingerprints were randomized into a model derivation and model validation group. Predictive model was derived using multiple logistic regression. Validation was done using the goodness-of-fit test. The fingerprint verification prediction model consists of a major criterion (fingerprint dystrophy area of ≥ 25%) and two minor criteria (long horizontal lines and long vertical lines). The presence of the major criterion predicts it will almost always fail verification, while presence of both minor criteria and presence of one minor criterion predict high and low risk of fingerprint verification failure, respectively. When none of the criteria are met, the fingerprint almost always passes the verification. The area under the receiver operating characteristic curve was 0.937, and the goodness-of-fit test showed agreement between the observed and expected number (P = 0.26). The derived fingerprint verification failure prediction model is validated and highly discriminatory in predicting risk of fingerprint verification in patients with hand dermatitis. © 2014 The International Society of Dermatology.

  18. Validation of In Vitro Cell-Based Human Blood-Brain Barrier Model Using Clinical Positron Emission Tomography Radioligands To Predict In Vivo Human Brain Penetration

    International Nuclear Information System (INIS)

    Mabondzo, A.; Guyot, A.C.; Bottlaender, M.; Deverre, J.R.; Tsaouin, K.; Balimane, P.V.

    2010-01-01

    We have evaluated a novel in vitro cell-based human blood-brain barrier (BBB) model that could predict in vivo human brain penetration for compounds with different BBB permeabilities using the clinical positron emission tomography (PET) data. Comparison studies were also performed to demonstrate that the in vitro cell-based human BBB model resulted in better predictivity over the traditional permeability model in discovery organizations, Caco-2 cells. We evaluated the in vivo BBB permeability of [ 18 F] and [ 11 C]-compounds in humans by PET imaging. The in vivo plasma-brain exchange parameters used for comparison were determined in humans by PET using a kinetic analysis of the radiotracer binding. For each radiotracer, the parameters were determined by fitting the brain kinetics of the radiotracer using a two-tissue compartment model of the ligand-receptor interaction. Bidirectional transport studies with the same compounds as in in vivo studies were carried out using the in vitro cell-based human BBB model as well as Caco-2 cells. The in vitro cell-based human BBB model has important features of the BBB in vivo and is suitable for discriminating between CNS and non-CNS marketed drugs. A very good correlation (r 2 =0.90; P≤0.001) was demonstrated between in vitro BBB permeability and in vivo permeability coefficient. In contrast, a poor correlation (r 2 = 0.17) was obtained between Caco-2 data and in vivo human brain penetration. This study highlights the potential of this in vitro cell-based human BBB model in drug discovery and shows that it can be an extremely effective screening tool for CNS programs. (authors)

  19. Validity of at home model predictions as a proxy for personal exposure to radiofrequency electromagnetic fields from mobile phone base stations.

    Science.gov (United States)

    Martens, Astrid L; Bolte, John F B; Beekhuizen, Johan; Kromhout, Hans; Smid, Tjabe; Vermeulen, Roel C H

    2015-10-01

    Epidemiological studies on the potential health effects of RF-EMF from mobile phone base stations require efficient and accurate exposure assessment methods. Previous studies have demonstrated that the 3D geospatial model NISMap is able to rank locations by indoor and outdoor RF-EMF exposure levels. This study extends on previous work by evaluating the suitability of using NISMap to estimate indoor RF-EMF exposure levels at home as a proxy for personal exposure to RF-EMF from mobile phone base stations. For 93 individuals in the Netherlands we measured personal exposure to RF-EMF from mobile phone base stations during a 24h period using an EME-SPY 121 exposimeter. Each individual kept a diary from which we extracted the time spent at home and in the bedroom. We used NISMap to model exposure at the home address of the participant (at bedroom height). We then compared model predictions with measurements for the 24h period, when at home, and in the bedroom by the Spearman correlation coefficient (rsp) and by calculating specificity and sensitivity using the 90th percentile of the exposure distribution as a cutpoint for high exposure. We found a low to moderate rsp of 0.36 for the 24h period, 0.51 for measurements at home, and 0.41 for measurements in the bedroom. The specificity was high (0.9) but with a low sensitivity (0.3). These results indicate that a meaningful ranking of personal RF-EMF can be achieved, even though the correlation between model predictions and 24h personal RF-EMF measurements is lower than with at home measurements. However, the use of at home RF-EMF field predictions from mobile phone base stations in epidemiological studies leads to significant exposure misclassification that will result in a loss of statistical power to detect health effects. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Validation of the Risk Prediction Models STATE-Score and START-Strategy to Guide TACE Treatment in Patients with Hepatocellular Carcinoma.

    Science.gov (United States)

    Mähringer-Kunz, Aline; Kloeckner, Roman; Pitton, Michael B; Düber, Christoph; Schmidtmann, Irene; Galle, Peter R; Koch, Sandra; Weinmann, Arndt

    2017-07-01

    Several scoring systems that guide patients' treatment regimen for transarterial chemoembolization (TACE) of hepatocellular carcinoma (HCC) have been introduced, but none have gained widespread acceptance in clinical practice. The purpose of this study is to externally validate the Selection for TrAnsarterial chemoembolization TrEatment (STATE)-score and START-strategy [i.e., sequential use of the STATE-score and Assessment for Retreatment with TACE (ART)-score]. From January 2000 to September 2015, 933 patients with HCC underwent TACE at our institution. All variables needed to calculate the STATE-score and implement the START-strategy were determined. STATE comprised serum albumin, up-to-seven criteria, and C-reactive protein (CRP). ART comprised an increase in aspartate aminotransferase, the Child-Pugh score, and a radiological tumor response. Overall survival was calculated, and multivariate analysis performed. In addition, the STATE-score and START-strategy were validated using the Harrell's C-index and integrated Brier score (IBS). The STATE-score was calculated in 228 patients. Low and high STATE-scores corresponded to median survival of 14.3 and 20.2 months, respectively. Harrell's C was 0.558 and IBS 0.133. For the STATE-score, significant predictors of survival were up-to-seven criteria (p = 0.006) and albumin (p = 0.022). CRP values were not predictive (p = 0.367). The ART-score was calculated in 207 patients. Combining the STATE-score and ART-score led to a Harrell's C of 0.580 and IBS of 0.132. The STATE-score was unable to reliably determine the suitability for initial TACE. The START-strategy only slightly improved the predictive ability compared to the ART-score alone. Therefore, neither the STATE-score nor START-strategy alone provides sufficient certainty for clear-cut clinical decisions.

  1. Cross Validating Ocean Prediction and Monitoring Systems

    National Research Council Canada - National Science Library

    Mooers, Christopher; Meinen, Christopher; Baringer, Molly; Bang, Inkweon; Rhodes, Robert C; Barron, Charlie N; Bub, Frank

    2005-01-01

    With the ongoing development of ocean circulation models and real-time observing systems, routine estimation of the synoptic state of the ocean is becoming feasible for practical and scientific purposes...

  2. Validation of a phytoremediation computer model

    International Nuclear Information System (INIS)

    Corapcioglu, M.Y.; Sung, K.; Rhykerd, R.L.; Munster, C.; Drew, M.

    1999-01-01

    The use of plants to stimulate remediation of contaminated soil is an effective, low-cost cleanup method which can be applied to many different sites. A phytoremediation computer model has been developed to simulate how recalcitrant hydrocarbons interact with plant roots in unsaturated soil. A study was conducted to provide data to validate and calibrate the model. During the study, lysimeters were constructed and filled with soil contaminated with 10 [mg kg -1 ] TNT, PBB and chrysene. Vegetated and unvegetated treatments were conducted in triplicate to obtain data regarding contaminant concentrations in the soil, plant roots, root distribution, microbial activity, plant water use and soil moisture. When given the parameters of time and depth, the model successfully predicted contaminant concentrations under actual field conditions. Other model parameters are currently being evaluated. 15 refs., 2 figs

  3. Validation of elk resource selection models with spatially independent data

    Science.gov (United States)

    Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson

    2011-01-01

    Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...

  4. The concept of validation of numerical models for consequence analysis

    International Nuclear Information System (INIS)

    Borg, Audun; Paulsen Husted, Bjarne; Njå, Ove

    2014-01-01

    Numerical models such as computational fluid dynamics (CFD) models are increasingly used in life safety studies and other types of analyses to calculate the effects of fire and explosions. The validity of these models is usually established by benchmark testing. This is done to quantitatively measure the agreement between the predictions provided by the model and the real world represented by observations in experiments. This approach assumes that all variables in the real world relevant for the specific study are adequately measured in the experiments and in the predictions made by the model. In this paper the various definitions of validation for CFD models used for hazard prediction are investigated to assess their implication for consequence analysis in a design phase. In other words, how is uncertainty in the prediction of future events reflected in the validation process? The sources of uncertainty are viewed from the perspective of the safety engineer. An example of the use of a CFD model is included to illustrate the assumptions the analyst must make and how these affect the prediction made by the model. The assessments presented in this paper are based on a review of standards and best practice guides for CFD modeling and the documentation from two existing CFD programs. Our main thrust has been to assess how validation work is performed and communicated in practice. We conclude that the concept of validation adopted for numerical models is adequate in terms of model performance. However, it does not address the main sources of uncertainty from the perspective of the safety engineer. Uncertainty in the input quantities describing future events, which are determined by the model user, outweighs the inaccuracies in the model as reported in validation studies. - Highlights: • Examine the basic concept of validation applied to models for consequence analysis. • Review standards and guides for validation of numerical models. • Comparison of the validation

  5. Concepts of Model Verification and Validation

    International Nuclear Information System (INIS)

    Thacker, B.H.; Doebling, S.W.; Hemez, F.M.; Anderson, M.C.; Pepin, J.E.; Rodriguez, E.A.

    2004-01-01

    Model verification and validation (VandV) is an enabling methodology for the development of computational models that can be used to make engineering predictions with quantified confidence. Model VandV procedures are needed by government and industry to reduce the time, cost, and risk associated with full-scale testing of products, materials, and weapon systems. Quantifying the confidence and predictive accuracy of model calculations provides the decision-maker with the information necessary for making high-consequence decisions. The development of guidelines and procedures for conducting a model VandV program are currently being defined by a broad spectrum of researchers. This report reviews the concepts involved in such a program. Model VandV is a current topic of great interest to both government and industry. In response to a ban on the production of new strategic weapons and nuclear testing, the Department of Energy (DOE) initiated the Science-Based Stockpile Stewardship Program (SSP). An objective of the SSP is to maintain a high level of confidence in the safety, reliability, and performance of the existing nuclear weapons stockpile in the absence of nuclear testing. This objective has challenged the national laboratories to develop high-confidence tools and methods that can be used to provide credible models needed for stockpile certification via numerical simulation. There has been a significant increase in activity recently to define VandV methods and procedures. The U.S. Department of Defense (DoD) Modeling and Simulation Office (DMSO) is working to develop fundamental concepts and terminology for VandV applied to high-level systems such as ballistic missile defense and battle management simulations. The American Society of Mechanical Engineers (ASME) has recently formed a Standards Committee for the development of VandV procedures for computational solid mechanics models. The Defense Nuclear Facilities Safety Board (DNFSB) has been a proponent of model

  6. Finding Furfural Hydrogenation Catalysts via Predictive Modelling.

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-09-10

    We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (k(H):k(D)=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R(2)=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model's predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.

  7. Finding Furfural Hydrogenation Catalysts via Predictive Modelling

    Science.gov (United States)

    Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

    2010-01-01

    Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

  8. Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques.

    Science.gov (United States)

    Amin, Sk Abdul; Bhargava, Sonam; Adhikari, Nilanjan; Gayen, Shovanlal; Jha, Tarun

    2018-02-01

    Phosphodiesterase 1 (PDE1) is a potential target for a number of neurodegenerative disorders such as Schizophrenia, Parkinson's and Alzheimer's diseases. A number of pyrazolo[3,4-d]pyrimidine PDE1 inhibitors were subjected to different molecular modelling techniques [such as regression-based quantitative structure-activity relationship (QSAR): multiple linear regression, support vector machine and artificial neural network; classification-based QSAR: Bayesian modelling and Recursive partitioning; Monte Carlo based QSAR; Open3DQSAR; pharmacophore mapping and molecular docking analyses] to get a detailed knowledge about the physicochemical and structural requirements for higher inhibitory activity. The planarity of the pyrimidinone ring plays an important role for PDE1 inhibition. The N-methylated function at the 5th position of the pyrazolo[3,4-d]pyrimidine core is required for interacting with the PDE1 enzyme. The cyclopentyl ring fused with the parent scaffold is necessary for PDE1 binding potency. The phenylamino substitution at 3rd position is crucial for PDE1 inhibition. The N2-substitution at the pyrazole moiety is important for PDE1 inhibition compared to the N1-substituted analogues. Moreover, the p-substituted benzyl side chain at N2-position helps to enhance the PDE1 inhibitory profile. Depending on these observations, some new molecules are predicted that may possess better PDE1 inhibition.

  9. System Advisor Model: Flat Plate Photovoltaic Performance Modeling Validation Report

    Energy Technology Data Exchange (ETDEWEB)

    Freeman, Janine [National Renewable Energy Lab. (NREL), Golden, CO (United States); Whitmore, Jonathan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Kaffine, Leah [National Renewable Energy Lab. (NREL), Golden, CO (United States); Blair, Nate [National Renewable Energy Lab. (NREL), Golden, CO (United States); Dobos, Aron P. [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2013-12-01

    The System Advisor Model (SAM) is a free software tool that performs detailed analysis of both system performance and system financing for a variety of renewable energy technologies. This report provides detailed validation of the SAM flat plate photovoltaic performance model by comparing SAM-modeled PV system generation data to actual measured production data for nine PV systems ranging from 75 kW to greater than 25 MW in size. The results show strong agreement between SAM predictions and field data, with annualized prediction error below 3% for all fixed tilt cases and below 8% for all one axis tracked cases. The analysis concludes that snow cover and system outages are the primary sources of disagreement, and other deviations resulting from seasonal biases in the irradiation models and one axis tracking issues are discussed in detail.

  10. Field validation of the contaminant transport model, FEMA

    International Nuclear Information System (INIS)

    Wong, K.-F.V.

    1986-01-01

    The work describes the validation with field data of a finite element model of material transport through aquifers (FEMA). Field data from the Idaho Chemical Processing Plant, Idaho, USA and from the 58th Street landfill in Miami, Florida, USA are used. In both cases the model was first calibrated and then integrated over a span of eight years to check on the predictive capability of the model. Both predictive runs gave results that matched well with available data. (author)

  11. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  12. Prospective validation of pathologic complete response models in rectal cancer: Transferability and reproducibility.

    Science.gov (United States)

    van Soest, Johan; Meldolesi, Elisa; van Stiphout, Ruud; Gatta, Roberto; Damiani, Andrea; Valentini, Vincenzo; Lambin, Philippe; Dekker, Andre

    2017-09-01

    Multiple models have been developed to predict pathologic complete response (pCR) in locally advanced rectal cancer patients. Unfortunately, validation of these models normally omit the implications of cohort differences on prediction model performance. In this work, we will perform a prospective validation of three pCR models, including information whether this validation will target transferability or reproducibility (cohort differences) of the given models. We applied a novel methodology, the cohort differences model, to predict whether a patient belongs to the training or to the validation cohort. If the cohort differences model performs well, it would suggest a large difference in cohort characteristics meaning we would validate the transferability of the model rather than reproducibility. We tested our method in a prospective validation of three existing models for pCR prediction in 154 patients. Our results showed a large difference between training and validation cohort for one of the three tested models [Area under the Receiver Operating Curve (AUC) cohort differences model: 0.85], signaling the validation leans towards transferability. Two out of three models had a lower AUC for validation (0.66 and 0.58), one model showed a higher AUC in the validation cohort (0.70). We have successfully applied a new methodology in the validation of three prediction models, which allows us to indicate if a validation targeted transferability (large differences between training/validation cohort) or reproducibility (small cohort differences). © 2017 American Association of Physicists in Medicine.

  13. Geochemistry Model Validation Report: Material Degradation and Release Model

    Energy Technology Data Exchange (ETDEWEB)

    H. Stockman

    2001-09-28

    The purpose of this Analysis and Modeling Report (AMR) is to validate the Material Degradation and Release (MDR) model that predicts degradation and release of radionuclides from a degrading waste package (WP) in the potential monitored geologic repository at Yucca Mountain. This AMR is prepared according to ''Technical Work Plan for: Waste Package Design Description for LA'' (Ref. 17). The intended use of the MDR model is to estimate the long-term geochemical behavior of waste packages (WPs) containing U. S . Department of Energy (DOE) Spent Nuclear Fuel (SNF) codisposed with High Level Waste (HLW) glass, commercial SNF, and Immobilized Plutonium Ceramic (Pu-ceramic) codisposed with HLW glass. The model is intended to predict (1) the extent to which criticality control material, such as gadolinium (Gd), will remain in the WP after corrosion of the initial WP, (2) the extent to which fissile Pu and uranium (U) will be carried out of the degraded WP by infiltrating water, and (3) the chemical composition and amounts of minerals and other solids left in the WP. The results of the model are intended for use in criticality calculations. The scope of the model validation report is to (1) describe the MDR model, and (2) compare the modeling results with experimental studies. A test case based on a degrading Pu-ceramic WP is provided to help explain the model. This model does not directly feed the assessment of system performance. The output from this model is used by several other models, such as the configuration generator, criticality, and criticality consequence models, prior to the evaluation of system performance. This document has been prepared according to AP-3.10Q, ''Analyses and Models'' (Ref. 2), and prepared in accordance with the technical work plan (Ref. 17).

  14. Geochemistry Model Validation Report: Material Degradation and Release Model

    International Nuclear Information System (INIS)

    Stockman, H.

    2001-01-01

    The purpose of this Analysis and Modeling Report (AMR) is to validate the Material Degradation and Release (MDR) model that predicts degradation and release of radionuclides from a degrading waste package (WP) in the potential monitored geologic repository at Yucca Mountain. This AMR is prepared according to ''Technical Work Plan for: Waste Package Design Description for LA'' (Ref. 17). The intended use of the MDR model is to estimate the long-term geochemical behavior of waste packages (WPs) containing U. S . Department of Energy (DOE) Spent Nuclear Fuel (SNF) codisposed with High Level Waste (HLW) glass, commercial SNF, and Immobilized Plutonium Ceramic (Pu-ceramic) codisposed with HLW glass. The model is intended to predict (1) the extent to which criticality control material, such as gadolinium (Gd), will remain in the WP after corrosion of the initial WP, (2) the extent to which fissile Pu and uranium (U) will be carried out of the degraded WP by infiltrating water, and (3) the chemical composition and amounts of minerals and other solids left in the WP. The results of the model are intended for use in criticality calculations. The scope of the model validation report is to (1) describe the MDR model, and (2) compare the modeling results with experimental studies. A test case based on a degrading Pu-ceramic WP is provided to help explain the model. This model does not directly feed the assessment of system performance. The output from this model is used by several other models, such as the configuration generator, criticality, and criticality consequence models, prior to the evaluation of system performance. This document has been prepared according to AP-3.10Q, ''Analyses and Models'' (Ref. 2), and prepared in accordance with the technical work plan (Ref. 17)

  15. Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

    Science.gov (United States)

    Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G M; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M

    2014-01-01

    In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53) to 0.63 (95% CI, 0.60-0.66). The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

  16. Some considerations for validation of repository performance assessment models

    International Nuclear Information System (INIS)

    Eisenberg, N.

    1991-01-01

    Validation is an important aspect of the regulatory uses of performance assessment. A substantial body of literature exists indicating the manner in which validation of models is usually pursued. Because performance models for a nuclear waste repository cannot be tested over the long time periods for which the model must make predictions, the usual avenue for model validation is precluded. Further impediments to model validation include a lack of fundamental scientific theory to describe important aspects of repository performance and an inability to easily deduce the complex, intricate structures characteristic of a natural system. A successful strategy for validation must attempt to resolve these difficulties in a direct fashion. Although some procedural aspects will be important, the main reliance of validation should be on scientific substance and logical rigor. The level of validation needed will be mandated, in part, by the uses to which these models are put, rather than by the ideal of validation of a scientific theory. Because of the importance of the validation of performance assessment models, the NRC staff has engaged in a program of research and international cooperation to seek progress in this important area. 2 figs., 16 refs

  17. Predictive Validity And Usefulness Of Visual Scanning Task In Hiv ...

    African Journals Online (AJOL)

    The visual scanning task is a useful screening tool for brain damage in HIV/AIDS by inference from impairment of visual information processing and disturbances in perceptual mental strategies. There is progressive neuro-cognitive decline as the disease worsens. Keywords: brain, cognition, HIV/AIDS, predictive validity, ...

  18. A robust approach to QMU, validation, and conservative prediction.

    Energy Technology Data Exchange (ETDEWEB)

    Segalman, Daniel Joseph; Paez, Thomas Lee; Bauman, Lara E

    2013-01-01

    A systematic approach to defining margin in a manner that incorporates statistical information and accommodates data uncertainty, but does not require assumptions about specific forms of the tails of distributions is developed. This approach extends to calculations underlying validation assessment and quantitatively conservative predictions.

  19. Prediction and validation of hemodialysis duration in acute methanol poisoning.

    Science.gov (United States)

    Lachance, Philippe; Mac-Way, Fabrice; Desmeules, Simon; De Serres, Sacha A; Julien, Anne-Sophie; Douville, Pierre; Ghannoum, Marc; Agharazii, Mohsen

    2015-11-01

    The duration of hemodialysis (HD) in methanol poisoning (MP) is dependent on the methanol concentration, the operational parameters used during HD, and the presence and severity of metabolic acidosis. However, methanol assays are not easily available, potentially leading to undue extension or premature termination of treatment. Here we provide a prediction model for the duration of high-efficiency HD in MP. In a retrospective cohort study, we identified 71 episodes of MP in 55 individuals who were treated with alcohol dehydrogenase inhibition and HD. Four patients had residual visual abnormality at discharge and only one patient died. In 46 unique episodes of MP with high-efficiency HD the mean methanol elimination half-life (T1/2) during HD was 108 min in women, significantly different from the 129 min in men. In a training set of 28 patients with MP, using the 90th percentile of gender-specific elimination T1/2 (147 min in men and 141 min in women) and a target methanol concentration of 4 mmol/l allowed all cases to reach a safe methanol of under 6 mmol/l. The prediction model was confirmed in a validation set of 18 patients with MP. High-efficiency HD time in hours can be estimated using 3.390 × (Ln (MCi/4)) for women and 3.534 × (Ln (MCi/4)) for men, where MCi is the initial methanol concentration in mmol/l, provided that metabolic acidosis is corrected.

  20. PREDICTED PERCENTAGE DISSATISFIED (PPD) MODEL ...

    African Journals Online (AJOL)

    HOD

    their low power requirements, are relatively cheap and are environment friendly. ... PREDICTED PERCENTAGE DISSATISFIED MODEL EVALUATION OF EVAPORATIVE COOLING ... The performance of direct evaporative coolers is a.

  1. On Validation of Directional Wave Predictions: Review and Discussion

    National Research Council Canada - National Science Library

    Rogers, W. E; Wang, David W

    2006-01-01

    This report consists of supplementary materials for an article, accepted for publication in the "Journal of Atmospheric and Oceanic Technology," dealing with directional wave model validation by the same authors...

  2. Validation of an in vitro model for predicting rumen and total-tract fiber digestibility in dairy cows fed corn silages with different in vitro neutral detergent fiber digestibilities at 2 levels of dry matter intake.

    Science.gov (United States)

    Lopes, F; Cook, D E; Combs, D K

    2015-01-01

    An in vivo study was performed to validate an in vitro procedure that predicts rate of fiber digestion and total-tract neutral detergent fiber digestibility (TTNDFD). Two corn silages that differed in fiber digestibility were used in this trial. The corn silage with lower fiber digestibility (LFDCS) had the TTNDFD prediction of 36.0% of total NDF, whereas TTNDFD for the corn silage with higher fiber digestibility (HFDCS) was 44.9% of total neutral detergent fiber (NDF). Two diets (1 with LFDCS and 1 with HFDCS) were formulated and analyzed using the in vitro assay to predict the TTNDFD and rumen potentially digestible NDF (pdNDF) digestion rate. Similar diets were fed to 8 ruminally cannulated, multiparous, high-producing dairy cows in 2 replicated 4×4 Latin squares with 21-d periods. A 2×2 factorial arrangement of treatments was used with main effects of intake (restricted to approximately 90% of ad libitum intake vs. ad libitum) and corn silage of different fiber digestibility. Treatments were restricted and ad libitum LFDCS as well as restricted and ad libitum HFDCS. The input and output values predicted from the in vitro model were compared with in vivo measurements. The pdNDF intake predicted by the in vitro model was similar to pdNDF intake observed in vivo. Also, the pdNDF digestion rate predicted in vitro was similar to what was observed in vivo. The in vitro method predicted TTNDFD of 50.2% for HFDCS and 42.9% for LFDCS as a percentage of total NDF in the diets, whereas the in vivo measurements of TTNDFD averaged 50.3 and 48.6% of total NDF for the HFDCS and LFDCS diets, respectively. The in vitro TTNDFD assay predicted total-tract NDF digestibility of HFDCS diets similar to the digestibility observed in vivo, but for LFDCS diets the assay underestimated the digestibility compared with in vivo. When the in vitro and in vivo measurements were compared without intake effect (ad libitum and restricted) considering only diet effect of silage fiber

  3. Predictive performance models and multiple task performance

    Science.gov (United States)

    Wickens, Christopher D.; Larish, Inge; Contorer, Aaron

    1989-01-01

    Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

  4. Model-Based Method for Sensor Validation

    Science.gov (United States)

    Vatan, Farrokh

    2012-01-01

    Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

  5. Atmospheric corrosion: statistical validation of models

    International Nuclear Information System (INIS)

    Diaz, V.; Martinez-Luaces, V.; Guineo-Cobs, G.

    2003-01-01

    In this paper we discuss two different methods for validation of regression models, applied to corrosion data. One of them is based on the correlation coefficient and the other one is the statistical test of lack of fit. Both methods are used here to analyse fitting of bi logarithmic model in order to predict corrosion for very low carbon steel substrates in rural and urban-industrial atmospheres in Uruguay. Results for parameters A and n of the bi logarithmic model are reported here. For this purpose, all repeated values were used instead of using average values as usual. Modelling is carried out using experimental data corresponding to steel substrates under the same initial meteorological conditions ( in fact, they are put in the rack at the same time). Results of correlation coefficient are compared with the lack of it tested at two different signification levels (α=0.01 and α=0.05). Unexpected differences between them are explained and finally, it is possible to conclude, at least in the studied atmospheres, that the bi logarithmic model does not fit properly the experimental data. (Author) 18 refs

  6. Statistical Validation of Engineering and Scientific Models: Background

    International Nuclear Information System (INIS)

    Hills, Richard G.; Trucano, Timothy G.

    1999-01-01

    A tutorial is presented discussing the basic issues associated with propagation of uncertainty analysis and statistical validation of engineering and scientific models. The propagation of uncertainty tutorial illustrates the use of the sensitivity method and the Monte Carlo method to evaluate the uncertainty in predictions for linear and nonlinear models. Four example applications are presented; a linear model, a model for the behavior of a damped spring-mass system, a transient thermal conduction model, and a nonlinear transient convective-diffusive model based on Burger's equation. Correlated and uncorrelated model input parameters are considered. The model validation tutorial builds on the material presented in the propagation of uncertainty tutoriaI and uses the damp spring-mass system as the example application. The validation tutorial illustrates several concepts associated with the application of statistical inference to test model predictions against experimental observations. Several validation methods are presented including error band based, multivariate, sum of squares of residuals, and optimization methods. After completion of the tutorial, a survey of statistical model validation literature is presented and recommendations for future work are made

  7. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  8. Selection, calibration, and validation of models of tumor growth.

    Science.gov (United States)

    Lima, E A B F; Oden, J T; Hormuth, D A; Yankeelov, T E; Almeida, R C

    2016-11-01

    This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as "model agnostic" in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology ( in vivo ). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction-diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory

  9. Validation of resting metabolic rate prediction equations for teenagers

    Directory of Open Access Journals (Sweden)

    Paulo Henrique Santos da Fonseca

    2007-09-01

    Full Text Available The resting metabolic rate (RMR can be defi ned as the minimum rate of energy spent and represents the main component of the energetic outlay. The purpose of this study is to validate equations to predict the resting metabolic rate in teenagers (103 individuals, being 51 girls and 52 boys, with age between 10 and 17 years from Florianópolis – SC – Brazil. It was measured: the body weight, body height, skinfolds and obtained the lean and body fat mass through bioimpedance. The nonproteic RMR was measured by Weir’s equation (1949, utilizing AeroSport TEEM-100 gas analyzer. The studied equations were: Harry and Benedict (1919, Schofi eld (1985, WHO/FAO/UNU (1985, Henry and Rees (1991, Molnár et al. (1998, Tverskaya et al. (1998 and Müller et al. (2004. In order to study the cross-validation of the RMR prediction equations and its standard measure (Weir 1949, the following statistics procedure were calculated: Pearson’s correlation (r ≥ 0.70, the “t” test with the signifi cance level of p0.05 in relation to the standard measure, with exception of the equations suggested for Tverskaya et al. (1998, and the two models of Müller et al (2004. Even though there was not a signifi cant difference, only the models considered for Henry and Rees (1991, and Molnár et al. (1995 had gotten constant error variation under 5%. All the equations analyzed in the study in girls had not reached criterion of correlation values of 0.70 with the indirect calorimetry. Analyzing the prediction equations of RMR in boys, all of them had moderate correlation coeffi cients with the indirect calorimetry, however below 0.70. Only the equation developed for Tverskaya et al. (1998 presented differences (p ABSTRACT0,05 em relação à medida padrão (Weir 1949, com exceção das equações sugeridas por Tverskaya et al. (1998 e os dois modelos de Müller et al (2004. Mesmo não havendo diferença signifi cativa, somente os modelos propostos por Henry e Rees (1991

  10. MODEL PREDICTIVE CONTROL FUNDAMENTALS

    African Journals Online (AJOL)

    2012-07-02

    Jul 2, 2012 ... signal based on a process model, coping with constraints on inputs and ... paper, we will present an introduction to the theory and application of MPC with Matlab codes ... section 5 presents the simulation results and section 6.

  11. Face-to-face comparison of the predictive validity of two models of neuropathic pain in the rat: analgesic activity of pregabalin, tramadol and duloxetine.

    Science.gov (United States)

    Le Cudennec, Camille; Castagné, Vincent

    2014-07-15

    We compared the preclinical analgesic activity of three marketed drugs with different pharmacological properties, pregabalin, tramadol and duloxetine, described as effective against neuropathic pain in the clinic. These drugs were tested against evoked pain in two different neuropathic models in the rat, the Bennett (CCI) and the Chung (SNL) models. The selected endpoints were tactile allodynia, tactile hyperalgesia, heat hyperalgesia and cold allodynia. Although all three drugs displayed analgesic activity, the effects observed varied according to the behavioral evaluation. Pregabalin showed clear analgesic effects against cold allodynia and tactile hyperalgesia in both the CCI and Chung models. Tramadol was active against all four endpoints in the Chung model with similar effects in the CCI model, apart from tactile allodynia. Duloxetine inhibited tactile allodynia and heat hyperalgesia in both neuropathic pain models. It also displayed efficacy against tactile hyperalgesia in the CCI model and against cold allodynia in the Chung model. These data confirm that the CCI and the Chung models of neuropathic pain do not detect the activity of analgesics with the same sensitivity. Furthermore, the mode of stimulation (tactile or thermal) and the type of endpoint (allodynia or hyperalgesia) can further influence the observed efficacy of gold standards as well as novel compounds developed for treating neuropathic pain symptoms. Copyright © 2014. Published by Elsevier B.V.

  12. The Predictive Validity of the ABFM's In-Training Examination.

    Science.gov (United States)

    O'Neill, Thomas R; Li, Zijia; Peabody, Michael R; Lybarger, Melanie; Royal, Kenneth; Puffer, James C

    2015-05-01

    Our objective was to examine the predictive validity of the American Board of Family Medicine's (ABFM) In-Training Examination (ITE) with regard to predicting outcomes on the ABFM certification examination. This study used a repeated measures design across three levels of medical training (PGY1--PGY2, PGY2--PGY3, and PGY3--initial certification) with three different cohorts (2010--2011, 2011--2012, and 2012--2013) to examine: (1) how well the residents' ITE scores correlated with their test scores in the following year, (2) what the typical score increase was across training years, and (3) what was the sensitivity, specificity, positive predictive value, and negative predictive value of the PGY3 scores with regard to predicting future results on the MC-FP Examination. ITE scores generally correlate at about .7 with the following year's ITE or with the following year's certification examination. The mean growth from PGY1 to PGY2 was 52 points, from PGY2 to PGY3 was 34 points, and from PGY3 to initial certification was 27 points. The sensitivity, specificity, positive predictive value, and negative predictive value were .91, .47, .96, and .27, respectively. The ITE is a useful predictor of future ITE and initial certification examination performance.

  13. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites

    DEFF Research Database (Denmark)

    Zhou, Yanlian; Wu, Xiaocui; Ju, Weimin

    2015-01-01

    Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed...... to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using...... data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (epsilon(msh)) was 2.63 to 4.59 times that of sunlit leaves...

  14. Melanoma Risk Prediction Models

    Science.gov (United States)

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  15. Feature Extraction for Structural Dynamics Model Validation

    Energy Technology Data Exchange (ETDEWEB)

    Farrar, Charles [Los Alamos National Laboratory; Nishio, Mayuko [Yokohama University; Hemez, Francois [Los Alamos National Laboratory; Stull, Chris [Los Alamos National Laboratory; Park, Gyuhae [Chonnam Univesity; Cornwell, Phil [Rose-Hulman Institute of Technology; Figueiredo, Eloi [Universidade Lusófona; Luscher, D. J. [Los Alamos National Laboratory; Worden, Keith [University of Sheffield

    2016-01-13

    As structural dynamics becomes increasingly non-modal, stochastic and nonlinear, finite element model-updating technology must adopt the broader notions of model validation and uncertainty quantification. For example, particular re-sampling procedures must be implemented to propagate uncertainty through a forward calculation, and non-modal features must be defined to analyze nonlinear data sets. The latter topic is the focus of this report, but first, some more general comments regarding the concept of model validation will be discussed.

  16. Model Validation in Ontology Based Transformations

    Directory of Open Access Journals (Sweden)

    Jesús M. Almendros-Jiménez

    2012-10-01

    Full Text Available Model Driven Engineering (MDE is an emerging approach of software engineering. MDE emphasizes the construction of models from which the implementation should be derived by applying model transformations. The Ontology Definition Meta-model (ODM has been proposed as a profile for UML models of the Web Ontology Language (OWL. In this context, transformations of UML models can be mapped into ODM/OWL transformations. On the other hand, model validation is a crucial task in model transformation. Meta-modeling permits to give a syntactic structure to source and target models. However, semantic requirements have to be imposed on source and target models. A given transformation will be sound when source and target models fulfill the syntactic and semantic requirements. In this paper, we present an approach for model validation in ODM based transformations. Adopting a logic programming based transformational approach we will show how it is possible to transform and validate models. Properties to be validated range from structural and semantic requirements of models (pre and post conditions to properties of the transformation (invariants. The approach has been applied to a well-known example of model transformation: the Entity-Relationship (ER to Relational Model (RM transformation.

  17. Assessment model validity document FARF31

    International Nuclear Information System (INIS)

    Elert, Mark; Gylling Bjoern; Lindgren, Maria

    2004-08-01

    The prime goal of model validation is to build confidence in the model concept and that the model is fit for its intended purpose. In other words: Does the model predict transport in fractured rock adequately to be used in repository performance assessments. Are the results reasonable for the type of modelling tasks the model is designed for. Commonly, in performance assessments a large number of realisations of flow and transport is made to cover the associated uncertainties. Thus, the flow and transport including radioactive chain decay are preferably calculated in the same model framework. A rather sophisticated concept is necessary to be able to model flow and radionuclide transport in the near field and far field of a deep repository, also including radioactive chain decay. In order to avoid excessively long computational times there is a need for well-based simplifications. For this reason, the far field code FARF31 is made relatively simple, and calculates transport by using averaged entities to represent the most important processes. FARF31 has been shown to be suitable for the performance assessments within the SKB studies, e.g. SR 97. Among the advantages are that it is a fast, simple and robust code, which enables handling of many realisations with wide spread in parameters in combination with chain decay of radionuclides. Being a component in the model chain PROPER, it is easy to assign statistical distributions to the input parameters. Due to the formulation of the advection-dispersion equation in FARF31 it is possible to perform the groundwater flow calculations separately.The basis for the modelling is a stream tube, i.e. a volume of rock including fractures with flowing water, with the walls of the imaginary stream tube defined by streamlines. The transport within the stream tube is described using a dual porosity continuum approach, where it is assumed that rock can be divided into two distinct domains with different types of porosity

  18. Isotopes as validation tools for global climate models

    International Nuclear Information System (INIS)

    Henderson-Sellers, A.

    2001-01-01

    Global Climate Models (GCMs) are the predominant tool with which we predict the future climate. In order that people can have confidence in such predictions, GCMs require validation. As almost every available item of meteorological data has been exploited in the construction and tuning of GCMs to date, independent validation is very difficult. This paper explores the use of isotopes as a novel and fully independent means of evaluating GCMs. The focus is the Amazon Basin which has a long history of isotope collection and analysis and also of climate modelling: both having been reported for over thirty years. Careful consideration of the results of GCM simulations of Amazonian deforestation and climate change suggests that the recent stable isotope record is more consistent with the predicted effects of greenhouse warming, possibly combined with forest removal, than with GCM predictions of the effects of deforestation alone

  19. Predictive models of moth development

    Science.gov (United States)

    Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

  20. A predictive model for dimensional errors in fused deposition modeling

    DEFF Research Database (Denmark)

    Stolfi, A.

    2015-01-01

    This work concerns the effect of deposition angle (a) and layer thickness (L) on the dimensional performance of FDM parts using a predictive model based on the geometrical description of the FDM filament profile. An experimental validation over the whole a range from 0° to 177° at 3° steps and two...... values of L (0.254 mm, 0.330 mm) was produced by comparing predicted values with external face-to-face measurements. After removing outliers, the results show that the developed two-parameter model can serve as tool for modeling the FDM dimensional behavior in a wide range of deposition angles....

  1. In-Hospital Risk Prediction for Post-stroke Depression. Development and Validation of the Post-stroke Depression Prediction Scale

    NARCIS (Netherlands)

    Thóra Hafsteinsdóttir; Roelof G.A. Ettema; Diederick Grobbee; Prof. Dr. Marieke J. Schuurmans; Janneke van Man-van Ginkel; Eline Lindeman

    2013-01-01

    Background and Purpose—The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early

  2. Predictive Models and Computational Embryology

    Science.gov (United States)

    EPA’s ‘virtual embryo’ project is building an integrative systems biology framework for predictive models of developmental toxicity. One schema involves a knowledge-driven adverse outcome pathway (AOP) framework utilizing information from public databases, standardized ontologies...

  3. A broad view of model validation

    International Nuclear Information System (INIS)

    Tsang, C.F.

    1989-10-01

    The safety assessment of a nuclear waste repository requires the use of models. Such models need to be validated to ensure, as much as possible, that they are a good representation of the actual processes occurring in the real system. In this paper we attempt to take a broad view by reviewing step by step the modeling process and bringing out the need to validating every step of this process. This model validation includes not only comparison of modeling results with data from selected experiments, but also evaluation of procedures for the construction of conceptual models and calculational models as well as methodologies for studying data and parameter correlation. The need for advancing basic scientific knowledge in related fields, for multiple assessment groups, and for presenting our modeling efforts in open literature to public scrutiny is also emphasized. 16 refs

  4. Validation of models using Chernobyl fallout data from southern Finland. Scenario S. Second report of the VAMP multiple pathways assessment working group. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-09-01

    Following the Chernobyl accident and on the recommendation of the International Nuclear Safety Advisory Group (INSAG) in its Summary Report on the Post-Accident Review Meeting on the Chernobyl Accident (Safety Series No. 75-INSAG-1, IAEA, Vienna, 1986), the IAEA established a Co-ordinated Research Programme on ``The Validation of Models for the Transfer of Radionuclides in Terrestrial, Urban and Aquatic Environments and the Acquisition of Data for that Purpose``. The programme used the information on the environmental behaviour of radionuclides which became available as a result of the measurement programmes instituted in countries of the former Soviet Union and in many European countries after April 1986 for the purpose of testing the reliability of assessment models. Such models find application in assessing the radiological impact of all parts of the nuclear fuel cycle. They are used in the planning and design stage to predict the radiological impact of nuclear facilities and in assessing the possible consequences of accidents involving releases of radioactive material to the environment and in establishing criteria for the implementation of countermeasures. In the operational phase, they are used together with the results of environmental monitoring to demonstrate compliance with regulatory requirements concerned with radiation dose limitation. Refs, figs, tabs.

  5. Validation of models using Chernobyl fallout data from southern Finland. Scenario S. Second report of the VAMP multiple pathways assessment working group. Part of the IAEA/CEC co-ordinated research programme on the validation of environmental model predictions (VAMP)

    International Nuclear Information System (INIS)

    1996-09-01

    Following the Chernobyl accident and on the recommendation of the International Nuclear Safety Advisory Group (INSAG) in its Summary Report on the Post-Accident Review Meeting on the Chernobyl Accident (Safety Series No. 75-INSAG-1, IAEA, Vienna, 1986), the IAEA established a Co-ordinated Research Programme on ''The Validation of Models for the Transfer of Radionuclides in Terrestrial, Urban and Aquatic Environments and the Acquisition of Data for that Purpose''. The programme used the information on the environmental behaviour of radionuclides which became available as a result of the measurement programmes instituted in countries of the former Soviet Union and in many European countries after April 1986 for the purpose of testing the reliability of assessment models. Such models find application in assessing the radiological impact of all parts of the nuclear fuel cycle. They are used in the planning and design stage to predict the radiological impact of nuclear facilities and in assessing the possible consequences of accidents involving releases of radioactive material to the environment and in establishing criteria for the implementation of countermeasures. In the operational phase, they are used together with the results of environmental monitoring to demonstrate compliance with regulatory requirements concerned with radiation dose limitation. Refs, figs, tabs

  6. Calibration and validation of the SWAT model for predicting daily ET over irrigated crops in the Texas High Plains using lysimetric data

    Science.gov (United States)

    The Soil Water Assessment Tool (SWAT) is a widely used watershed model for simulating stream flow, overland flow, sediment, pesticide, and bacterial loading in response to management practices. All SWAT processes are directly dependent upon the accurate representation of hydrology. Evapotranspiratio...

  7. Calibration and validation of the SWAT model for predicting daily ET for irrigated crops in the Texas High Plains using lysimetric data

    Science.gov (United States)

    The Soil and Water Assessment Tool (SWAT) model has been used to assess the impacts of alternative agricultural management practices on non-point source pollution in watersheds of various topography and scale throughout the world. Water balance is the driving force behind all processes of SWAT, as i...

  8. Validation of forcefields in predicting the physical and thermophysical properties of emeraldine base polyaniline

    NARCIS (Netherlands)

    Chen, X.P.; Yuan, C.A.; Wong, C.K.Y.; Koh, S.W.; Zhang, G.Q.

    2011-01-01

    We report a molecular modelling study to validate the forcefields [condensed-phase optimised molecular potentials for atomistic simulation studies (COMPASS) and polymer-consistent forcefield (PCFF)] in predicting the physical and thermophysical properties of polymers. This work comprises of two key

  9. Prediction models for successful external cephalic version: a systematic review.

    Science.gov (United States)

    Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

    2015-12-01

    To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

  10. Slip Validation and Prediction for Mars Exploration Rovers

    Directory of Open Access Journals (Sweden)

    Jeng Yen

    2008-04-01

    Full Text Available This paper presents a novel technique to validate and predict the rover slips on Martian surface for NASA’s Mars Exploration Rover mission (MER. Different from the traditional approach, the proposed method uses the actual velocity profile of the wheels and the digital elevation map (DEM from the stereo images of the terrain to formulate the equations of motion. The six wheel speed from the empirical encoder data comprises the vehicle's velocity, and the rover motion can be estimated using mixed differential and algebraic equations. Applying the discretization operator to these equations, the full kinematics state of the rover is then resolved by the configuration kinematics solution in the Rover Sequencing and Visualization Program (RSVP. This method, with the proper wheel slip and sliding factors, produces accurate simulation of the Mars Exploration rovers, which have been validated with the earth-testing vehicle. This computational technique has been deployed to the operation of the MER rovers in the extended mission period. Particularly, it yields high quality prediction of the rover motion on high slope areas. The simulated path of the rovers has been validated using the telemetry from the onboard Visual Odometry (VisOdom. Preliminary results indicate that the proposed simulation is very effective in planning the path of the rovers on the high-slope areas.

  11. Base Flow Model Validation, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The innovation is the systematic "building-block" validation of CFD/turbulence models employing a GUI driven CFD code (RPFM) and existing as well as new data sets to...

  12. Model validation: Correlation for updating

    Indian Academy of Sciences (India)

    In this paper, a review is presented of the various methods which ... to make a direct and objective comparison of specific dynamic properties, measured ..... stiffness matrix is available from the analytical model, is that of reducing or condensing.

  13. Validating EHR clinical models using ontology patterns.

    Science.gov (United States)

    Martínez-Costa, Catalina; Schulz, Stefan

    2017-12-01

    Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit. This paper demonstrates how ontology design patterns can validate existing clinical models using SHACL. Based on the Clinical Information Modelling Initiative (CIMI), we show how ontology patterns detect both modeling and terminology binding errors in CIMI models. SHACL, a W3C constraint language for the validation of RDF graphs, builds on the concept of "Shape", a description of data in terms of expected cardinalities, datatypes and other restrictions. SHACL, as opposed to OWL, subscribes to the Closed World Assumption (CWA) and is therefore more suitable for the validation of clinical models. We have demonstrated the feasibility of the approach by manually describing the correspondences between six CIMI clinical models represented in RDF and two SHACL ontology design patterns. Using a Java-based SHACL implementation, we found at least eleven modeling and binding errors within these CIMI models. This demonstrates the usefulness of ontology design patterns not only as a modeling tool but also as a tool for validation. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Verification and validation for waste disposal models

    International Nuclear Information System (INIS)

    1987-07-01

    A set of evaluation criteria has been developed to assess the suitability of current verification and validation techniques for waste disposal methods. A survey of current practices and techniques was undertaken and evaluated using these criteria with the items most relevant to waste disposal models being identified. Recommendations regarding the most suitable verification and validation practices for nuclear waste disposal modelling software have been made

  15. Tracer travel time and model validation

    International Nuclear Information System (INIS)

    Tsang, Chin-Fu.

    1988-01-01

    The performance assessment of a nuclear waste repository demands much more in comparison to the safety evaluation of any civil constructions such as dams, or the resource evaluation of a petroleum or geothermal reservoir. It involves the estimation of low probability (low concentration) of radionuclide transport extrapolated 1000's of years into the future. Thus models used to make these estimates need to be carefully validated. A number of recent efforts have been devoted to the study of this problem. Some general comments on model validation were given by Tsang. The present paper discusses some issues of validation in regards to radionuclide transport. 5 refs

  16. Validation of mathematical models for predicting the swirling flow and the vortex rope in a Francis turbine operated at partial discharge

    DEFF Research Database (Denmark)

    Kuibin, P.A.; Okulov, Valery; Susan-Resiga, R.F.

    2010-01-01

    recover all this information without actually computing the full three-dimensional unsteady flow in the hydraulic turbine. As a result, we provide valuable mathematical tools for assessing the turbine behaviour at off-design operating regimes in the early stages of runner design, with computational effort......The vortex rope in a hydro turbine draft tube is one the main and strong sources of pulsations in non-optimal modes of hydro turbine operation. We examine the case of a Francis turbine model operated at partial discharge, where a strong precessing vortex rope is developed in the discharge cone...... several orders of magnitude less than the current approaches of simulating the complex turbine flow....

  17. Predicting survival of de novo metastatic breast cancer in Asian women: systematic review and validation study.

    Directory of Open Access Journals (Sweden)

    Hui Miao

    Full Text Available BACKGROUND: In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. MATERIALS AND METHODS: We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic. RESULTS: We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48-0.53 to 0.63 (95% CI, 0.60-0.66. CONCLUSION: The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

  18. Implementation and validation of an economic module in the Be-FAST model to predict costs generated by livestock disease epidemics: Application to classical swine fever epidemics in Spain.

    Science.gov (United States)

    Fernández-Carrión, E; Ivorra, B; Martínez-López, B; Ramos, A M; Sánchez-Vizcaíno, J M

    2016-04-01

    Be-FAST is a computer program based on a time-spatial stochastic spread mathematical model for studying the transmission of infectious livestock diseases within and between farms. The present work describes a new module integrated into Be-FAST to model the economic consequences of the spreading of classical swine fever (CSF) and other infectious livestock diseases within and between farms. CSF is financially one of the most damaging diseases in the swine industry worldwide. Specifically in Spain, the economic costs in the two last CSF epidemics (1997 and 2001) reached jointly more than 108 million euros. The present analysis suggests that severe CSF epidemics are associated with significant economic costs, approximately 80% of which are related to animal culling. Direct costs associated with control measures are strongly associated with the number of infected farms, while indirect costs are more strongly associated with epidemic duration. The economic model has been validated with economic information around the last outbreaks in Spain. These results suggest that our economic module may be useful for analysing and predicting economic consequences of livestock disease epidemics. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Shape of the self-concept clarity change during group psychotherapy predicts the outcome: an empirical validation of the theoretical model of the self-concept change

    Science.gov (United States)

    Styła, Rafał

    2015-01-01

    Background: Self-Concept Clarity (SCC) describes the extent to which the schemas of the self are internally integrated, well defined, and temporally stable. This article presents a theoretical model that describes how different shapes of SCC change (especially stable increase and “V” shape) observed in the course of psychotherapy are related to the therapy outcome. Linking the concept of Jean Piaget and the dynamic systems theory, the study postulates that a stable SCC increase is needed for the participants with a rather healthy personality structure, while SCC change characterized by a “V” shape or fluctuations is optimal for more disturbed patients. Method: Correlational study in a naturalistic setting with repeated measurements (M = 5.8) was conducted on the sample of 85 patients diagnosed with neurosis and personality disorders receiving intensive eclectic group psychotherapy under routine inpatient conditions. Participants filled in the Self-Concept Clarity Scale (SCCS), Symptoms' Questionnaire KS-II, and Neurotic Personality Questionnaire KON-2006 at the beginning and at the end of the course of psychotherapy. The SCCS was also administered every 2 weeks during psychotherapy. Results: As hypothesized, among the relatively healthiest group of patients the stable SCC increase was related to positive treatment outcome, while more disturbed patients benefited from the fluctuations and “V” shape of SCC change. Conclusions: The findings support the idea that for different personality dispositions either a monotonic increase or transient destabilization of SCC is a sign of a good treatment prognosis. PMID:26579001

  20. Shape of the self-concept clarity change during group psychotherapy predicts the outcome: An empirical validation of the theoretical model of the self-concept change

    Directory of Open Access Journals (Sweden)

    Rafał eStyła

    2015-10-01

    Full Text Available Background: Self-concept clarity describes the extent to which the schemas of the self are internally integrated, well defined, and temporally stable. This article presents a theoretical model that describes how different shapes of self-concept clarity change (especially stable increase and V shape observed in the course of psychotherapy are related to the therapy outcome. Linking the concept of Jean Piaget and the dynamic systems theory, the study postulates that a stable self-concept clarity increase is needed for the participants with a rather healthy personality structure, while self-concept clarity change characterized by a V shape or fluctuations is optimal for more disturbed patients. Method: Correlational study in a naturalistic setting with repeated measurements (M=5.8 was conducted on the sample of 85 patients diagnosed with neurosis and personality disorders receiving intensive eclectic group psychotherapy under routine inpatient conditions. Participants filled in the Self-Concept Clarity Scale, Symptoms’ Questionnaire KS-II, and Neurotic Personality Questionnaire KON-2006 at the beginning and at the end of the course of psychotherapy. The Self-Concept Clarity Scale was also administered every two weeks during psychotherapy. Results: As hypothesized, among the relatively healthiest group of patients the stable self-concept clarity increase was related to positive treatment outcome, while more disturbed patients benefited from the fluctuations and V shape of self-concept clarity change. Conclusions: The findings support the idea that for different personality dispositions either a monotonic increase or transient destabilization of self-concept clarity is a sign of a good treatment prognosis.

  1. Development and validation of a prognostic model to predict death in patients with traumatic bleeding, and evaluation of the effect of tranexamic acid on mortality according to baseline risk: a secondary analysis of a randomised controlled trial.

    Science.gov (United States)

    Perel, P; Prieto-Merino, D; Shakur, H; Roberts, I

    2013-06-01

    Severe bleeding accounts for about one-third of in-hospital trauma deaths. Patients with a high baseline risk of death have the most to gain from the use of life-saving treatments. An accurate and user-friendly prognostic model to predict mortality in bleeding trauma patients could assist doctors and paramedics in pre-hospital triage and could shorten the time to diagnostic and life-saving procedures such as surgery and tranexamic acid (TXA). The aim of the study was to develop and validate a prognostic model for early mortality in patients with traumatic bleeding and to examine whether or not the effect of TXA on the risk of death and thrombotic events in bleeding adult trauma patients varies according to baseline risk. Multivariable logistic regression and risk-stratified analysis of a large international cohort of trauma patients. Two hundred and seventy-four hospitals in 40 high-, medium- and low-income countries. We derived prognostic models in a large placebo-controlled trial of the effects of early administration of a short course of TXA [Clinical Randomisation of an Antifibrinolytic in Significant Haemorrhage (CRASH-2) trial]. The trial included 20,127 trauma patients with, or at risk of, significant bleeding, within 8 hours of injury. We externally validated the model on 14,220 selected trauma patients from the Trauma Audit and Research Network (TARN), which included mainly patients from the UK. We examined the effect of TXA on all-cause mortality, death due to bleeding and thrombotic events (fatal and non-fatal myocardial infarction, stroke, deep-vein thrombosis and pulmonary embolism) within risk strata in the CRASH-2 trial data set and we estimated the proportion of premature deaths averted by applying the odds ratio (OR) from the CRASH-2 trial to each of the risk strata in TARN. For the stratified analysis according baseline risk we considered the intervention TXA (1 g over 10 minutes followed by 1 g over 8 hours) or matching placebo. For the

  2. The predictive validity of ideal partner preferences: a review and meta-analysis.

    Science.gov (United States)

    Eastwick, Paul W; Luchies, Laura B; Finkel, Eli J; Hunt, Lucy L

    2014-05-01

    A central element of interdependence theory is that people have standards against which they compare their current outcomes, and one ubiquitous standard in the mating domain is the preference for particular attributes in a partner (ideal partner preferences). This article reviews research on the predictive validity of ideal partner preferences and presents a new integrative model that highlights when and why ideals succeed or fail to predict relational outcomes. Section 1 examines predictive validity by reviewing research on sex differences in the preference for physical attractiveness and earning prospects. Men and women reliably differ in the extent to which these qualities affect their romantic evaluations of hypothetical targets. Yet a new meta-analysis spanning the attraction and relationships literatures (k = 97) revealed that physical attractiveness predicted romantic evaluations with a moderate-to-strong effect size (r = ∼.40) for both sexes, and earning prospects predicted romantic evaluations with a small effect size (r = ∼.10) for both sexes. Sex differences in the correlations were small (r difference = .03) and uniformly nonsignificant. Section 2 reviews research on individual differences in ideal partner preferences, drawing from several theoretical traditions to explain why ideals predict relational evaluations at different relationship stages. Furthermore, this literature also identifies alternative measures of ideal partner preferences that have stronger predictive validity in certain theoretically sensible contexts. Finally, a discussion highlights a new framework for conceptualizing the appeal of traits, the difference between live and hypothetical interactions, and the productive interplay between mating research and broader psychological theories.

  3. Two stage neural network modelling for robust model predictive control.

    Science.gov (United States)

    Patan, Krzysztof

    2018-01-01

    The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  4. Validating agent based models through virtual worlds.

    Energy Technology Data Exchange (ETDEWEB)

    Lakkaraju, Kiran; Whetzel, Jonathan H.; Lee, Jina; Bier, Asmeret Brooke; Cardona-Rivera, Rogelio E.; Bernstein, Jeremy Ray Rhythm

    2014-01-01

    As the US continues its vigilance against distributed, embedded threats, understanding the political and social structure of these groups becomes paramount for predicting and dis- rupting their attacks. Agent-based models (ABMs) serve as a powerful tool to study these groups. While the popularity of social network tools (e.g., Facebook, Twitter) has provided extensive communication data, there is a lack of ne-grained behavioral data with which to inform and validate existing ABMs. Virtual worlds, in particular massively multiplayer online games (MMOG), where large numbers of people interact within a complex environ- ment for long periods of time provide an alternative source of data. These environments provide a rich social environment where players engage in a variety of activities observed between real-world groups: collaborating and/or competing with other groups, conducting battles for scarce resources, and trading in a market economy. Strategies employed by player groups surprisingly re ect those seen in present-day con icts, where players use diplomacy or espionage as their means for accomplishing their goals. In this project, we propose to address the need for ne-grained behavioral data by acquiring and analyzing game data a commercial MMOG, referred to within this report as Game X. The goals of this research were: (1) devising toolsets for analyzing virtual world data to better inform the rules that govern a social ABM and (2) exploring how virtual worlds could serve as a source of data to validate ABMs established for analogous real-world phenomena. During this research, we studied certain patterns of group behavior to compliment social modeling e orts where a signi cant lack of detailed examples of observed phenomena exists. This report outlines our work examining group behaviors that underly what we have termed the Expression-To-Action (E2A) problem: determining the changes in social contact that lead individuals/groups to engage in a particular behavior

  5. Advanced validation of CFD-FDTD combined method using highly applicable solver for reentry blackout prediction

    International Nuclear Information System (INIS)

    Takahashi, Yusuke

    2016-01-01

    An analysis model of plasma flow and electromagnetic waves around a reentry vehicle for radio frequency blackout prediction during aerodynamic heating was developed in this study. The model was validated based on experimental results from the radio attenuation measurement program. The plasma flow properties, such as electron number density, in the shock layer and wake region were obtained using a newly developed unstructured grid solver that incorporated real gas effect models and could treat thermochemically non-equilibrium flow. To predict the electromagnetic waves in plasma, a frequency-dependent finite-difference time-domain method was used. Moreover, the complicated behaviour of electromagnetic waves in the plasma layer during atmospheric reentry was clarified at several altitudes. The prediction performance of the combined model was evaluated with profiles and peak values of the electron number density in the plasma layer. In addition, to validate the models, the signal losses measured during communication with the reentry vehicle were directly compared with the predicted results. Based on the study, it was suggested that the present analysis model accurately predicts the radio frequency blackout and plasma attenuation of electromagnetic waves in plasma in communication. (paper)

  6. The Reliability and Predictive Validity of the Stalking Risk Profile.

    Science.gov (United States)

    McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

    2018-03-01

    This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

  7. Be-CoDiS: A Mathematical Model to Predict the Risk of Human Diseases Spread Between Countries--Validation and Application to the 2014-2015 Ebola Virus Disease Epidemic.

    Science.gov (United States)

    Ivorra, Benjamin; Ngom, Diène; Ramos, Ángel M

    2015-09-01

    Ebola virus disease is a lethal human and primate disease that currently requires a particular attention from the international health authorities due to important outbreaks in some Western African countries and isolated cases in the UK, the USA and Spain. Regarding the emergency of this situation, there is a need for the development of decision tools, such as mathematical models, to assist the authorities to focus their efforts in important factors to eradicate Ebola. In this work, we propose a novel deterministic spatial-temporal model, called Between-Countries Disease Spread (Be-CoDiS), to study the evolution of human diseases within and between countries. The main interesting characteristics of Be-CoDiS are the consideration of the movement of people between countries, the control measure effects and the use of time-dependent coefficients adapted to each country. First, we focus on the mathematical formulation of each component of the model and explain how its parameters and inputs are obtained. Then, in order to validate our approach, we consider two numerical experiments regarding the 2014-2015 Ebola epidemic. The first one studies the ability of the model in predicting the EVD evolution between countries starting from the index cases in Guinea in December 2013. The second one consists of forecasting the evolution of the epidemic by using some recent data. The results obtained with Be-CoDiS are compared to real data and other model outputs found in the literature. Finally, a brief parameter sensitivity analysis is done. A free MATLAB version of Be-CoDiS is available at: http://www.mat.ucm.es/momat/software.htm.

  8. Model validation: a systemic and systematic approach

    International Nuclear Information System (INIS)

    Sheng, G.; Elzas, M.S.; Cronhjort, B.T.

    1993-01-01

    The term 'validation' is used ubiquitously in association with the modelling activities of numerous disciplines including social, political natural, physical sciences, and engineering. There is however, a wide range of definitions which give rise to very different interpretations of what activities the process involves. Analyses of results from the present large international effort in modelling radioactive waste disposal systems illustrate the urgent need to develop a common approach to model validation. Some possible explanations are offered to account for the present state of affairs. The methodology developed treats model validation and code verification in a systematic fashion. In fact, this approach may be regarded as a comprehensive framework to assess the adequacy of any simulation study. (author)

  9. Risk terrain modeling predicts child maltreatment.

    Science.gov (United States)

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  10. Ground-water models: Validate or invalidate

    Science.gov (United States)

    Bredehoeft, J.D.; Konikow, Leonard F.

    1993-01-01

    The word validation has a clear meaning to both the scientific community and the general public. Within the scientific community the validation of scientific theory has been the subject of philosophical debate. The philosopher of science, Karl Popper, argued that scientific theory cannot be validated, only invalidated. Popper’s view is not the only opinion in this debate; however, many scientists today agree with Popper (including the authors). To the general public, proclaiming that a ground-water model is validated carries with it an aura of correctness that we do not believe many of us who model would claim. We can place all the caveats we wish, but the public has its own understanding of what the word implies. Using the word valid with respect to models misleads the public; verification carries with it similar connotations as far as the public is concerned. Our point is this: using the terms validation and verification are misleading, at best. These terms should be abandoned by the ground-water community.

  11. Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models

    Science.gov (United States)

    van der Wijk, Lars; Proost, Johannes H.; Sinha, Bhanu; Touw, Daan J.

    2017-01-01

    Gentamicin shows large variations in half-life and volume of distribution (Vd) within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1) creating an optimal model for endocarditis patients; and 2) assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE) and Median Absolute Prediction Error (MDAPE) were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients) with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358) renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076) L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68%) as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37%) and standard (MDPE -0.90%, MDAPE 4.82%) models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to avoid

  12. Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models.

    Directory of Open Access Journals (Sweden)

    Anna Gomes

    Full Text Available Gentamicin shows large variations in half-life and volume of distribution (Vd within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1 creating an optimal model for endocarditis patients; and 2 assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE and Median Absolute Prediction Error (MDAPE were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358 renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076 L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68% as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37% and standard (MDPE -0.90%, MDAPE 4.82% models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to

  13. Predicting child maltreatment: A meta-analysis of the predictive validity of risk assessment instruments.

    Science.gov (United States)

    van der Put, Claudia E; Assink, Mark; Boekhout van Solinge, Noëlle F

    2017-11-01

    Risk assessment is crucial in preventing child maltreatment since it can identify high-risk cases in need of child protection intervention. Despite widespread use of risk assessment instruments in child welfare, it is unknown how well these instruments predict maltreatment and what instrument characteristics are associated with higher levels of predictive validity. Therefore, a multilevel meta-analysis was conducted to examine the predictive accuracy of (characteristics of) risk assessment instruments. A literature search yielded 30 independent studies (N=87,329) examining the predictive validity of 27 different risk assessment instruments. From these studies, 67 effect sizes could be extracted. Overall, a medium significant effect was found (AUC=0.681), indicating a moderate predictive accuracy. Moderator analyses revealed that onset of maltreatment can be better predicted than recurrence of maltreatment, which is a promising finding for early detection and prevention of child maltreatment. In addition, actuarial instruments were found to outperform clinical instruments. To bring risk and needs assessment in child welfare to a higher level, actuarial instruments should be further developed and strengthened by distinguishing risk assessment from needs assessment and by integrating risk assessment with case management. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibra