ABSTRACT
Background
Studies across medical specialties have shown that scores on residency self-assessment examinations (SAEs) can predict performance on certifying board examinations.
Objective
This study explored the predictive abilities of different composite SAE scores in physical medicine and rehabilitation and determined an optimal cut-point to identify an “at-risk” performance group.
Methods
For our study, both predictive scores (SAE scores) and outcomes (board examination scores) are expressed in national percentile scores. We analyzed data in graduates of a physical medicine and rehabilitation residency program between 2008 and 2014. We compared mean, median, lowest, highest, and most recent score among up to 3 SAE scores with respect to their associations with the outcome via linear and logistic regression. We computed regression/correlation coefficient, P value, R2, area under the curve, sensitivity, specificity, and predictive values. Identification of optimal cut-point was guided by accuracy, discrimination, and model-fit statistics.
Results
Predictor and outcome data were available for 88 of 99 residents. In regression models, all SAE predictors showed significant associations (P ≤ .001) and the mean score performed best (r = 0.55). A 1-point increase in mean SAE was associated with a 1.88 score increase in board score and a 16% decrease in odds of failure. The rule of mean SAE score below 47 yielded the highest accuracy, highest discrimination, and best model fit.
Conclusions
Mean SAE score may be used to predict performance on the American Board of Physical Medicine and Rehabilitation–written examination. The optimal statistical cut-point to identify the at-risk group for failure appears to be around the 47th SAE national percentile.
Predicting Performance on the American Board of Physical Medicine and Rehabilitation Written Examination Using Resident Self-Assessment Examination Scores