Association Between USMLE Step 1 Scores and In-Training Examination Performance: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: On February 12, 2020, the sponsors of the United States Medical Licensing Examination announced that Step 1 will transition to pass/fail scoring in 2022. Step 1 performance has historically carried substantial weight in the evaluation of residency applicants and as a predictor of subsequent subject-specific medical knowledge. Using a systematic review and meta-analysis, the authors sought to determine the association between Step 1 scores and in-training examination (ITE) performance, which is often used to assess knowledge acquisition during residency. METHOD: The authors systematically searched Medline, EMBASE, and Web of Science for observational studies published from 1992 through May 10, 2020. Observational studies reporting associations between Step 1 and ITE scores, regardless of medical or surgical specialty, were eligible for inclusion. Pairs of researchers screened all studies, evaluated quality assessment using a modified Newcastle-Ottawa Scale, and extracted data in a standardized fashion. The primary endpoint was the correlation of Step 1 and ITE scores. RESULTS: Of 1,432 observational studies identified, 49 were systematically reviewed and 37 were included in the meta-analysis. Overall study quality was low to moderate. The pooled estimate of the correlation coefficient was 0.42 (95% confidence interval [CI]: 0.36, 0.48; P < .001), suggesting a weak-to-moderate positive correlation between Step 1 and ITE scores. The random-effects meta-regression found the association between Step 1 and ITE scores was weaker for surgical (versus medical) specialties (beta -0.25 [95% CI: -0.41, -0.09; P = .003]) and fellowship (versus residency) training programs (beta -0.25 [95% CI: -0.47, -0.03; P = .030]). CONCLUSIONS: The authors identified a weak-to-moderate positive correlation between Step 1 and ITE scores based on a meta-analysis of low-to-moderate quality observational data. With Step 1 scoring transitioning to pass/fail, the undergraduate and graduate medical education communities should continue to develop better tools for evaluating medical students.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".