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Self Perceptions as Predictors for Return to Work 2 Years After Rehabilitation in Orthopedic Trauma Inpatients

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      Abstract

      Purpose This study aimed to identify self-perception variables which may predict return to work (RTW) in orthopedic trauma patients 2 years after rehabilitation. Methods A prospective cohort investigated 1,207 orthopedic trauma inpatients, hospitalised in rehabilitation, clinics at admission, discharge, and 2 years after discharge. Information on potential predictors was obtained from self administered questionnaires. Multiple logistic regression models were applied. Results In the final model, a higher likelihood of RTW was predicted by: better general health and lower pain at admission; health and pain improvements during hospitalisation; lower impact of event (IES-R) avoidance behaviour score; higher IES-R hyperarousal score, higher SF-36 mental score and low perceived severity of the injury. Conclusion RTW is not only predicted by perceived health, pain and severity of the accident at the beginning of a rehabilitation program, but also by the changes in pain and health perceptions observed during hospitalisation.

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      Most cited references 43

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        Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
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            Author and article information

            Affiliations
            [1 ]Département de l’appareil locomoteur, Clinique Romande de Réadaptation SUVA Care, Avenue Grand-Champsec 90, 1951 Sion, Switzerland
            [2 ]Research Department, Clinique Romande de Réadaptation SUVA Care, Sion, Switzerland
            [3 ]Institut de recherche en réadaptation et Service de recherche médicale, Clinique Romande de Réadaptation SUVA Care, Sion, Switzerland
            [4 ]Department of Vocational Rehabilitation, Rehaklinik Bellikon, Bellikon, Switzerland
            [5 ]Département de l’appareil locomoteur, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
            [6 ]Institute of Social and Preventive Medicine (IUMSP), Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
            Contributors
            +41-27-6032156 , +41-27-6032214 , maria.iakova@crr-suva.ch
            Journal
            J Occup Rehabil
            J Occup Rehabil
            Journal of Occupational Rehabilitation
            Springer US (Boston )
            1053-0487
            1573-3688
            5 May 2012
            5 May 2012
            December 2012
            : 22
            : 4
            : 532-540
            22562093
            3484271
            9369
            10.1007/s10926-012-9369-x
            © The Author(s) 2012
            Categories
            Article
            Custom metadata
            © Springer Science+Business Media New York 2012

            Occupational & Environmental medicine

            return to work, injury, accident, pain, ptsd, ies-r

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