The Impact of Postgraduate Training and Timing on USMLE Step 3 Performance
Bibliographic record
Abstract
PURPOSE: This study examined the extent to which differences in clinical experience, gained in postgraduate training programs, affect performance on Step 3 of the United States Medical Licensing Examination (USMLE). METHOD: Subjects in the study were 36,805 U.S. and Canadian medical school graduates who took USMLE Step 3 for the first time between November 1999 and December 2002. Regression analyses examined the relation between length and type of postgraduate training and Step 3 score after controlling for prior performance on previous USMLE examinations. RESULTS: Results indicate that postgraduate training in programs that provide exposure to a broad range of patient problems, and continued training in such areas, improves performance on Step 3. CONCLUSIONS: Study data reaffirm the validity of the USMLE Step 3 examination, and the information found in the pattern of results across specialties may be useful to residents and program directors.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".