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A Generalizable Approach to Predicting Performance on USMLE Step 2 CK.

Jeffrey B Bird1, Doreen M Olvet1, Joanne M Willey1

  • 1Department of Science Education, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.

Advances in Medical Education and Practice
|August 30, 2022
PubMed
Summary

A new model predicts United States Medical Licensing Examination (USMLE) Step 2 CK scores using pre-clinical data, offering a valuable tool for medical educators. This approach aids in assessing student performance without the USMLE Step 1 score.

Keywords:
NBMEassessmentcareer advisingmatchprediction model

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Area of Science:

  • Medical Education
  • Assessment and Evaluation
  • Medical Licensing Examinations

Background:

  • The elimination of the USMLE Step 1 score necessitates alternative methods for evaluating medical student performance.
  • USMLE Step 2 CK performance is increasingly critical for residency program directors and clinical evaluations.
  • Previous predictive models for Step 2 CK often relied on Step 1 scores, which are no longer available.

Purpose of the Study:

  • To develop and validate a predictive model for USMLE Step 2 CK scores using pre-clinical and in-curriculum assessment data.
  • To assess the model's generalizability and predictive accuracy at various educational milestones.
  • To provide a reliable tool for educators in the absence of USMLE Step 1 data.

Main Methods:

  • A linear regression model was developed using assessment data (MCAT, NBME CAS, Subject exams) from 228 students across three cohorts.
  • The model predicted USMLE Step 2 CK scores at five time-points: end of year 1, end of year 2, and three points during year 3.
  • A fourth cohort (class of 2021) was used for external validation of the predictive model.

Main Results:

  • The predictive model demonstrated increasing accuracy as students progressed through medical school, with adjusted R-squared values ranging from 0.29 (end of year 1) to 0.62 (end of year 3).
  • Inclusion of USMLE Step 1 scores did not significantly improve the final model's predictive power.
  • Validation on the independent cohort showed the model predicted Step 2 CK scores with a mean square error (MSE) of 8.3 points at the end of year 1, improving to 5.4 points by the end of year 3.

Conclusions:

  • The developed model offers a generalizable method for predicting USMLE Step 2 CK performance.
  • This tool allows medical educators to forecast student success as early as the first year, even without USMLE Step 1 scores.
  • Predictive accuracy incrementally improves throughout the clerkship year, providing timely insights for student support and program evaluation.