Predicting Step 2 CK Performance Using Automated Feature Selection and Nested Cross-Validation.

Background: Given the recent transition of the USMLE Step 1 exam to a pass/fail system, evaluative emphasis is expected to shift toward the USMLE Step 2 Clinical Knowledge (USMLE-CK) exam, prompting the need for advanced predictive models. In this study, we proposed a multiple linear regression approach incorporating automated feature selection to predict students' performance on the USMLE-CK exam. Methods: Our methodology integrated feature selection and model validation processes within a nested cross-validation (CV) framework to predict USMLE-CK scores. We conducted our analysis on data from four undergraduate medical student cohorts (Classes 2020 to 2023 inclusive, n = 117). A range of performance data was included for feature selection, including internal assessment data and National Board Medical Examination (NBME) Results: This led to the selection of a four-predictor model (adjusted-R Conclusion: Our approach effectively streamlined the process of building a predictive model by merging feature selection with model validation. By creating an interactive, user-friendly dashboard, we empower medical educators to predict students' USMLE-CK performance. This modeling and deployment approach holds promise for predicting student performances in other assessments.