Correlations Between the USMLE Step Examinations, American College of Physicians In-Training Examination, and ABIM Internal Medicine Certification Examination
Bibliographic record
Abstract
PURPOSE: To assess the correlations between United States Medical Licensing Examination (USMLE) performance, American College of Physicians Internal Medicine In-Training Examination (IM-ITE) performance, American Board of Internal Medicine Internal Medicine Certification Exam (IM-CE) performance, and other medical knowledge and demographic variables. METHOD: The study included 9,676 postgraduate year (PGY)-1, 11,424 PGY-2, and 10,239 PGY-3 internal medicine (IM) residents from any Accreditation Council for Graduate Medical Education-accredited IM residency program who took the IM-ITE (2014 or 2015) and the IM-CE (2015-2018). USMLE scores, IM-ITE percent correct scores, and IM-CE scores were analyzed using multiple linear regression, and IM-CE pass/fail status was analyzed using multiple logistic regression, controlling for USMLE Step 1, Step 2 Clinical Knowledge, and Step 3 scores; averaged medical knowledge milestones; age at IM-ITE; gender; and medical school location (United States or Canada vs international). RESULTS: All variables were significant predictors of passing the IM-CE with IM-ITE scores having the strongest association and USMLE Step scores being the next strongest predictors. Prediction curves for the probability of passing the IM-CE based solely on IM-ITE score for each PGY show that residents must score higher on the IM-ITE with each subsequent administration to maintain the same estimated probability of passing the IM-CE. CONCLUSIONS: The findings from this study should support residents and program directors in their efforts to more precisely identify and evaluate knowledge gaps for both personal learning and program improvement. While no individual USMLE Step score was as strongly predictive of IM-CE score as IM-ITE score, the combined relative contribution of all 3 USMLE Step scores was of a magnitude similar to that of IM-ITE score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".