Student Performances on Step 1 and Step 2 of the United States Medical Licensing Examination Following Implementation of a Problem-based Learning Curriculum
Bibliographic record
Abstract
PURPOSE: To examine students' performances on Step 1 and Step 2 of the United States Medical Licensing Examination (USMLE) following the implementation of a problem-based learning curriculum. METHOD: Performances on Step 1 of the USMLE for four classes at the University of Missouri-Columbia School of Medicine that completed a new problem-based learning curriculum (1997, 1998, 1999, and 2000) were compared with those of the last two classes to learn in the traditional curriculum (1995 and 1996). Performances on Step 2 of the USMLE for the classes of 1997, 1998, and 1999 were also compared with those of the classes of 1995 and 1996. The authors analyzed matriculation data (GPAs and MCAT scores) for all six classes. They compared all data with those of U.S. and Canadian first-time USMLE takers. RESULTS: The mean scores were higher on USMLE Step 1 for classes in the problem-based learning curriculum than for classes in the traditional curriculum. The mean scores for Step 2 were above the national mean for classes in the revised curriculum and below the national mean for classes in the traditional curriculum. The admission profiles of these classes were essentially the same before and after the change in curriculum. CONCLUSIONS: Major PBL revisions of the curriculum did not compromise the performances of medical students on the licensing examinations; in fact, they may have contributed to higher scores.
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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 | 0.001 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".