Surgery and Surgical Training Before Graduate Medical Education
Bibliographic record
Abstract
OBJECTIVE: To update and add to the first report commissioned by the Blue Ribbon Committee (BRC) about 20 years prior. BACKGROUND: Following a summit in late 2022 commissioned by the American Board of Surgery regarding competency-based reforms in surgical education and through a partnership with the American College of Surgeons and other stakeholders, a BRC-II on surgical education was formed. The BRC-II would have 7 subcommittees. This paper details the work of the Medical Student Subcommittee within the BRC-II. METHODS: The subcommittee's work, supported by staff from the American College of Surgeons, entailed a thorough literature review, which involved collating and aggregating the findings, identifying key challenges and opportunities, and committing to draft recommendations. These recommendations were then presented and refined through discussions with the BRC at large in multiple virtual and in-person settings. RESULTS: The subcommittee's work is detailed below and further summarized in table format. The section below elucidates the medical student education continuum and discusses the pertinent topics of recruitment, surgical engagement in medical student training and the surgical image, training for the current surgical practice model, trainee selection for graduate medical education, and optimizing the transition from undergraduate medical education to graduate medical education. CONCLUSIONS: The last 2 decades have shown significant changes and shifts in medical education and surgical practice. The findings of BRC-II in this manuscript help to structure the current and future necessary improvements, focusing on different aspects of medical student education.
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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 | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".