Our results support that medical career decisions are formed by a matching of perceptions of specialty characteristics with personal needs. However, the process of medical career decision-making is not yet fully understood. Besides identifying possible predictors, future research should focus on detecting interrelations between hypothesized predictors and identify the determinants and interrelations at the various stages of the medical career decision-making process.
The aim of this study was to investigate whether impaired ankle function after total ankle arthroplasty (TAA) affects the mechanical work during step-to-step transition and the metabolic cost of walking. Respiratory and force plate data were recorded in 11 patients and 11 healthy controls while they walked barefoot at a fixed walking speed (FWS, 1.25 m/s) and at their self-selected speed (SWS). At FWS metabolic cost of transport was 28% higher for the TAA group, but at SWS there was no significant increase. During the stepto-step transition, positive mechanical work generated by the trailing TAA leg was lower and negative mechanical work in the leading intact leg was larger. Despite the increase in mechanical work dissipation during double support, no significant differences in total mechanical work were found over a complete stride. This might be a result of methodological limitations of calculating mechanical work. Nevertheless, mechanical work dissipated during the step-to-step transition at FWS correlated significantly with metabolic cost of transport: r = .540. It was concluded that patients after successful TAA still experienced an impaired lower leg function, which contributed to an increase in mechanical energy dissipation during the step-to-step transition, and to an increase in the metabolic demand of walking.
BackgroundIn prognostic research, prediction rules are generally statistically derived. However the composition and performance of these statistical models may strongly depend on the characteristics of the derivation sample. The purpose of this study was to establish consensus among clinicians and experts on key predictors for persistent shoulder pain three months after initial consultation in primary care and assess the predictive performance of a model based on clinical expertise compared to a statistically derived model.MethodsA Delphi poll involving 3 rounds of data collection was used to reach consensus among health care professionals involved in the assessment and management of shoulder pain.ResultsPredictors selected by the expert panel were: symptom duration, pain catastrophizing, symptom history, fear-avoidance beliefs, coexisting neck pain, severity of shoulder disability, multisite pain, age, shoulder pain intensity and illness perceptions. When tested in a sample of 587 primary care patients consulting with shoulder pain the predictive performance of the two prognostic models based on clinical expertise were lower compared to that of a statistically derived model (Area Under the Curve, AUC, expert-based dichotomous predictors 0.656, expert-based continuous predictors 0.679 vs. 0.702 statistical model).ConclusionsThe three models were different in terms of composition, but all confirmed the prognostic importance of symptom duration, baseline level of shoulder disability and multisite pain. External validation in other populations of shoulder pain patients should confirm whether statistically derived models indeed perform better compared to models based on clinical expertise.
BackgroundIn prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.MethodsModels were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.ResultsResults showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.ConclusionIn prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.
BackgroundMany prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies.MethodsOutcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion.Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms.ResultsAfter consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies.ConclusionsDifferent methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies.
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