Institutional differences in USMLE Step 1 and 2 CK performance: Cross-sectional study of 89 US allopathic medical schools
Bibliographic record
Abstract
INTRODUCTION: The United States Medical Licensing Examination (USMLE) Step 1 and Step 2 Clinical Knowledge (CK) are important for trainee medical knowledge assessment and licensure, medical school program assessment, and residency program applicant screening. Little is known about how USMLE performance varies between institutions. This observational study attempts to identify institutions with above-predicted USMLE performance, which may indicate educational programs successful at promoting students' medical knowledge. METHODS: Self-reported institution-level data was tabulated from publicly available US News and World Report and Association of American Medical Colleges publications for 131 US allopathic medical schools from 2012-2014. Bivariate and multiple linear regression were performed. The primary outcome was institutional mean USMLE Step 1 and Step 2 CK scores outside a 95% prediction interval (≥2 standard deviations above or below predicted) based on multiple regression accounting for students' prior academic performance. RESULTS: Eighty-nine US medical schools (54 public, 35 private) reported complete USMLE scores over the three-year study period, representing over 39,000 examinees. Institutional mean grade point average (GPA) and Medical College Admission Test score (MCAT) achieved an adjusted R2 of 72% for Step 1 (standardized βMCAT 0.7, βGPA 0.2) and 41% for Step 2 CK (standardized βMCAT 0.5, βGPA 0.3) in multiple regression. Using this regression model, 5 institutions were identified with above-predicted institutional USMLE performance, while 3 institutions had below-predicted performance. CONCLUSIONS: This exploratory study identified several US allopathic medical schools with significant above- or below-predicted USMLE performance. Although limited by self-reported data, the findings raise questions about inter-institutional USMLE performance parity, and thus, educational parity. Additional work is needed to determine the etiology and robustness of the observed performance differences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".