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Susan D. Keller - One of the best experts on this subject based on the ideXlab platform.
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a 12 item short form health survey construction of scales and preliminary tests of reliability and validity
Medical Care, 1996Co-Authors: John E. Ware, Mark Kosinski, Susan D. KellerAbstract:Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n = 2,333). The resulting 12-item short-form (SF-12) achiev
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A 12-Item Short-Form Health Survey: Construction of Scales and Preliminary Tests of Reliability and Validity
Medical Care, 1996Co-Authors: John E. Ware, Michal Kosinski, Susan D. KellerAbstract:Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n=2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two Components (Physical Components Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week)correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n=232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of Physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery for depression. In 14 validity tests involving Physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median=0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 107 (median=0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.
John E. Ware - One of the best experts on this subject based on the ideXlab platform.
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a 12 item short form health survey construction of scales and preliminary tests of reliability and validity
Medical Care, 1996Co-Authors: John E. Ware, Mark Kosinski, Susan D. KellerAbstract:Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n = 2,333). The resulting 12-item short-form (SF-12) achiev
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A 12-Item Short-Form Health Survey: Construction of Scales and Preliminary Tests of Reliability and Validity
Medical Care, 1996Co-Authors: John E. Ware, Michal Kosinski, Susan D. KellerAbstract:Regression methods were used to select and score 12 items from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to reproduce the Physical Component Summary and Mental Component Summary scales in the general US population (n=2,333). The resulting 12-item short-form (SF-12) achieved multiple R squares of 0.911 and 0.918 in predictions of the SF-36 Physical Component Summary and SF-36 Mental Component Summary scores, respectively. Scoring algorithms from the general population used to score 12-item versions of the two Components (Physical Components Summary and Mental Component Summary) achieved R squares of 0.905 with the SF-36 Physical Component Summary and 0.938 with SF-36 Mental Component Summary when cross-validated in the Medical Outcomes Study. Test-retest (2-week)correlations of 0.89 and 0.76 were observed for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively, in the general US population (n=232). Twenty cross-sectional and longitudinal tests of empirical validity previously published for the 36-item short-form scales and summary measures were replicated for the 12-item Physical Component Summary and the 12-item Mental Component Summary, including comparisons between patient groups known to differ or to change in terms of the presence and seriousness of Physical and mental conditions, acute symptoms, age and aging, self-reported 1-year changes in health, and recovery for depression. In 14 validity tests involving Physical criteria, relative validity estimates for the 12-item Physical Component Summary ranged from 0.43 to 0.93 (median=0.67) in comparison with the best 36-item short-form scale. Relative validity estimates for the 12-item Mental Component Summary in 6 tests involving mental criteria ranged from 0.60 to 107 (median=0.97) in relation to the best 36-item short-form scale. Average scores for the 2 summary measures, and those for most scales in the 8-scale profile based on the 12-item short-form, closely mirrored those for the 36-item short-form, although standard errors were nearly always larger for the 12-item short-form.
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comparison of methods for the scoring and statistical analysis of sf 36 health profile and summary measures summary of results from the medical outcomes study
Medical Care, 1995Co-Authors: John E. Ware, Mark Kosinski, Martha S Bayliss, Colleen A Mchorney, William H Rogers, Anastasia E RaczekAbstract:Physical Component summary (PCS) and mental Component summary (MCS) measures make it possible to reduce the number of statistical comparisons and thereby the role of chance in testing hypotheses about health outcomes. To test their usefulness relative to a profile of eight scores, results were compared across 16 tests involving patients (N = 1,440) participating in the Medical Outcomes Study. Comparisons were made between groups known to differ at a point in time or to change over time in terms of age, diagnosis, severity of disease, comorbid conditions, acute symptoms, self-reported changes in health, and recovery from clinical depression. The relative validity (RV) of each measure was estimated by a comparison of statistical results with those for the best scales in the same tests.
Phil Marshall - One of the best experts on this subject based on the ideXlab platform.
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Physical Component analysis of galaxy cluster weak gravitational lensing data
Monthly Notices of the Royal Astronomical Society, 2006Co-Authors: Phil MarshallAbstract:We present a novel approach for reconstructing the projected mass distribution of clusters of galaxies from sparse and noisy weak gravitational lensing shear data. The reconstructions are regularized using knowledge gained from numerical simulations of clusters: trial mass distributions are constructed from n Physically motivated Components, each of which has the universal density profile and characteristic geometry observed in simulated clusters. The parameters of these Components are assumed to be distributed a priori in the same way as they are in the simulated clusters. Sampling mass distributions from the Components ‘parameters’ posterior probability density function allows estimates of the mass distribution to be generated, with error bars. The appropriate number of Components is inferred from the data itself via the Bayesian evidence, and is typically found to be small, reflecting the quality of the simulated data used in this work. Ensemble average mass maps are found to be robust to the details of the noise realization, and succeed in recovering the input mass distribution (from a realistic simulated cluster) over a wide range of scales. We comment on the residuals of the reconstruction and their implications, and discuss the extension of the method to include strong-lensing information.
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Physical Component analysis of galaxy cluster weak gravitational lensing data
arXiv: Astrophysics, 2005Co-Authors: Phil MarshallAbstract:We present a novel approach for reconstructing the projected mass distribution of clusters of galaxies from sparse and noisy weak gravitational lensing shear data. The reconstructions are regularised using knowledge gained from numerical simulations of clusters: trial mass distributions are constructed from N Physically-motivated Components, each of which has the universal density profile and characteristic geometry observed in simulated clusters. The parameters of these Components are assumed to be distributed \emph{a priori} in the same way as they are in the simulated clusters. Sampling mass distributions from the Components' parameters' posterior probability density function allows estimates of the mass distribution to be generated, with error bars. The appropriate number of Components is inferred from the data itself via the Bayesian evidence, and is typically found to be small, reflecting the quality of the simulated data used in this work. Ensemble average mass maps are found to be robust to the details of the noise realisation, and succeed in recovering the input mass distribution (from a realistic simulated cluster) over a wide range of scales. We comment on the residuals of the reconstruction and their implications, and discuss the extension of the method to include strong lensing information.
Helge Drange - One of the best experts on this subject based on the ideXlab platform.
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experimental and diagnostic protocol for the Physical Component of the cmip6 ocean model intercomparison project omip
Geoscientific Model Development Discussions, 2016Co-Authors: Stephen M Griffies, Gokhan Danabasoglu, Paul J Durack, Alistair Adcroft, V Balaji, Claus W Boning, Eric P Chassignet, Enrique N Curchitser, Julie Deshayes, Helge DrangeAbstract:The Ocean Model Intercomparison Project (OMIP) aims to provide a framework for evaluating, understanding, and improving the ocean and sea-ice Components of global climate and earth system models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). OMIP addresses these aims in two complementary manners: (A) by providing an experimental protocol for global ocean/sea-ice models run with a prescribed atmospheric forcing, (B) by providing a protocol for ocean diagnostics to be saved as part of CMIP6. We focus here on the Physical Component of OMIP, with a companion paper (Orr et al., 2016) offering details for the inert chemistry and interactive biogeochemistry. The Physical portion of the OMIP experimental protocol follows that of the interannual Coordinated Ocean-ice Reference Experiments (CORE-II). Since 2009, CORE-I (Normal Year Forcing) and CORE-II have become the standard method to evaluate global ocean/sea-ice simulations and to examine mechanisms for forced ocean climate variability. The OMIP diagnostic protocol is relevant for any ocean model Component of CMIP6, including the DECK (Diagnostic, Evaluation and Characterization of Klima experiments), historical simulations, FAFMIP (Flux Anomaly Forced MIP), C4MIP (Coupled Carbon Cycle Climate MIP), DAMIP (Detection and Attribution MIP), DCPP (Decadal Climate Prediction Project), ScenarioMIP (Scenario MIP), as well as the ocean-sea ice OMIP simulations. The bulk of this paper offers scientific rationale for saving these diagnostics.
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OMIP contribution to CMIP6: experimental and diagnostic protocol for the Physical Component of the Ocean Model Intercomparison Project
Geoscientific Model Development, 2016Co-Authors: Stephen M Griffies, Gokhan Danabasoglu, Paul J Durack, Alistair Adcroft, V Balaji, Claus W Boning, Eric P Chassignet, Enrique N Curchitser, Julie Deshayes, Helge DrangeAbstract:The Ocean Model Intercomparison Project (OMIP) is an endorsed project in the Coupled Model Intercomparison Project Phase 6 (CMIP6). OMIP addresses CMIP6 science questions, investigating the origins and consequences of systematic model biases. It does so by providing a framework for evaluating (including assessment of systematic biases), understanding, and improving ocean, sea-ice, tracer, and biogeochemical Components of climate and earth system models contributing to CMIP6. Among the WCRP Grand Challenges in climate science (GCs), OMIP primarily contributes to the regional sea level change and near-term (climate/decadal) prediction GCs. OMIP provides (a) an experimental protocol for global ocean/sea-ice models run with a prescribed atmospheric forcing; and (b) a protocol for ocean diagnostics to be saved as part of CMIP6. We focus here on the Physical Component of OMIP, with a companion paper (Orr et al., 2016) detailing methods for the inert chemistry and interactive biogeochemistry. The Physical portion of the OMIP experimental protocol follows the interannual Coordinated Ocean-ice Reference Experiments (CORE-II). Since 2009, CORE-I (Normal Year Forcing) and CORE-II (Interannual Forcing) have become the standard methods to evaluate global ocean/sea-ice simulations and to examine mechanisms for forced ocean climate variability. The OMIP diagnostic protocol is relevant for any ocean model Component of CMIP6, including the DECK (Diagnostic, Evaluation and Characterization of Klima experiments), historical simulations, FAFMIP (Flux Anomaly Forced MIP), C4MIP (Coupled Carbon Cycle Climate MIP), DAMIP (Detection and Attribution MIP), DCPP (Decadal Climate Prediction Project), ScenarioMIP, HighResMIP (High Resolution MIP), as well as the ocean/sea-ice OMIP simulations.
James H Roth - One of the best experts on this subject based on the ideXlab platform.
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responsiveness of the short form 36 disability of the arm shoulder and hand questionnaire patient rated wrist evaluation and Physical impairment measurements in evaluating recovery after a distal radius fracture
Journal of Hand Surgery (European Volume), 2000Co-Authors: Joy C Macdermid, Allan Donner, Robert S Richards, Nicolas Bellamy, James H RothAbstract:We evaluated the responsiveness of patient questionnaires and Physical testing in the assessment of recovery after distal radius fracture. Patients (n = 59) were assessed at their baseline clinic visit and again 3 and 6 months after injury. At each visit patients completed a short form-36, Disability of the Arm, Shoulder, and Hand questionnaire, and patient-rated wrist evaluation (PRWE). At 3 and 6 months grip strength, range of motion, and dexterity were analyzed. Standardized response means (SRM) and effects sizes were calculated to indicate responsiveness. The PRWE was the most responsive. Both the PRWE (SRM = 2.27) and the Disability of the Arm, Shoulder, and Hand (SRM = 2.01) questionnaire were more responsive than the short form-36 (SRM = 0.92). The Physical Component summary score of the short form-36 was similar to that of the Physical Component subscales. Questionnaires were highly responsive during the 0- to 3-month time period when Physical testing could not be performed. Of the Physical tests, grip strength was most responsive, followed by range of motion. Responsive patient-rating scales and Physical performance evaluations can assist with outcome evaluation of patients with distal radius fracture.
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responsiveness of the short form 36 disability of the arm shoulder and hand questionnaire patient rated wrist evaluation and Physical impairment measurements in evaluating recovery after a distal radius fracture
Journal of Hand Surgery (European Volume), 2000Co-Authors: Joy C Macdermid, Allan Donner, Robert S Richards, Nicolas Bellamy, James H RothAbstract:Abstract We evaluated the responsiveness of patient questionnaires and Physical testing in the assessment of recovery after distal radius fracture. Patients (n = 59) were assessed at their baseline clinic visit and again 3 and 6 months after injury. At each visit patients completed a short form-36, Disability of the Arm, Shoulder, and Hand questionnaire, and patient-rated wrist evaluation (PRWE). At 3 and 6 months grip strength, range of motion, and dexterity were analyzed. Standardized response means (SRM) and effects sizes were calculated to indicate responsiveness. The PRWE was the most responsive. Both the PRWE (SRM = 2.27) and the Disability of the Arm, Shoulder, and Hand (SRM = 2.01) questionnaire were more responsive than the short form-36 (SRM = 0.92). The Physical Component summary score of the short form-36 was similar to that of the Physical Component subscales. Questionnaires were highly responsive during the 0- to 3-month time period when Physical testing could not be performed. Of the Physical tests, grip strength was most responsive, followed by range of motion. Responsive patient-rating scales and Physical performance evaluations can assist with outcome evaluation of patients with distal radius fracture. (J Hand Surg 2000;25A:330–340. Copyright © 2000 by the American Society for Surgery of the Hand).
