Fourth-Year Medical School Course Load and Success as a Medical Intern
Bibliographic record
Abstract
BACKGROUND: The fourth year of medical school has come under recent scrutiny for its lack of structure, cost- and time-effectiveness, and quality of education it provides. Some have advocated for increasing clinical burden in the fourth year, while others have suggested it be abolished. OBJECTIVE: To assess the relationship between fourth-year course load and success during internship. METHODS: We reviewed transcripts of 78 internal medicine interns from 2011-2013 and compared the number of intensive courses (defined as subinternships, intensive care, surgical clerkships, and emergency medicine rotations) with multi-source performance evaluations from the internship. We assessed relative risk (RR) and 95% confidence interval (CI) of achieving excellent scores according to the number of intensive courses taken, using generalized estimating equations, adjusting for demographics, US Medical Licensing Examination (USMLE) Step 1 board scores, and other measures of medical school performance. RESULTS: = .03). An association of intensive course work with increased risk of excellent performance was seen across multiple clinical competencies, including medical knowledge (RR 1.08, 95% CI 1.04-1.11); patient care (RR 1.07, 95% CI 1.04-1.10); and practice-based learning (RR 1.05, 95% CI 1.03-1.09). CONCLUSIONS: For this single institution's cohort of medical interns, increased exposure to intensive course work during the fourth year of medical school was associated with better clinical evaluations during internship.
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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 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".