Multiple United States Medical Licensing Examination Attempts and the Estimated Risk of Disciplinary Actions Among Graduates of U.S. and Canadian Medical Schools
Bibliographic record
Abstract
PURPOSE: The United States Medical Licensing Examination (USMLE) recently announced 2 policy changes: shifting from numeric score reporting on the Step 1 examination to pass/fail reporting and limiting examinees to 4 attempts for each Step component. In light of these policies, exam measures other than scores, such as the number of examination attempts, are of interest. Attempt limit policies are intended to ensure minimum standards of physician competency, yet little research has explored how Step attempts relate to physician practice outcomes. This study examined the relationship between USMLE attempts and the likelihood of receiving disciplinary actions from state medical boards. METHOD: The sample population was 219,018 graduates from U.S. and Canadian MD-granting medical schools who passed all USMLE Step examinations by 2011 and obtained a medical license in the United States, using data from the NBME and the Federation of State Medical Boards. Logistic regressions estimated how attempts on Steps 1, 2 Clinical Knowledge (CK), and 3 examinations influenced the likelihood of receiving disciplinary actions by 2018, while accounting for physician characteristics. RESULTS: A total of 3,399 physicians (2%) received at least 1 disciplinary action. Additional attempts needed to pass Steps 1, 2 CK, and 3 were associated with an increased likelihood of receiving disciplinary actions (odds ratio [OR]: 1.07, 95% confidence interval [CI]: 1.01, 1.13; OR: 1.09, 95% CI: 1.03, 1.16; OR: 1.11, 95% CI: 1.04, 1.17, respectively), after accounting for other factors. CONCLUSIONS: Physicians who took multiple attempts to pass Steps 1, 2 CK, and 3 were associated with higher estimated likelihood of receiving disciplinary actions. This study offers support for licensure and practice standards to account for physicians' USMLE attempts. The relatively small effect sizes, however, caution policy makers from placing sole emphasis on this relationship.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".