The Blue Ribbon Committee II Report and Recommendations on Surgical Education and Training in the United States: 2024
Bibliographic record
Abstract
OBJECTIVE: An expert panel made recommendations to optimize surgical education and training based on the effects of contemporary challenges. BACKGROUND: The inaugural Blue Ribbon Committee (BRC I) proposed sweeping recommendations for surgical education and training in 2004. In light of those findings, a second BRC (BRC II) was convened to make recommendations to optimize surgical training considering the current landscape in medical education. METHODS: BRC II was a panel of 67 experts selected on the basis of experience and leadership in surgical education and training. It was organized into subcommittees which met virtually over the course of a year. They developed recommendations, along with the Steering Committee, based on areas of focus and then presented them to the entire BRC II. The Delphi method was chosen to obtain consensus, defined as ≥80% agreement among the panel. Cronbach α was computed to assess the internal consistency of 3 Delphi rounds. RESULTS: Of the 50 recommendations, 31 obtained consensus in the following aspects of surgical training (# of consensus recommendation/# of proposed): Workforce (1/5); Medical Student Education (3/8); Work Life Integration (4/6); Resident Education (5/7); Goals, Structure, and Financing of Training (5/8); Education Support and Faculty Development (5/6); Research Training (7/9); and Educational Technology and Assessment (1/1). The internal consistency was good in Rounds 1 and 2 and acceptable in Round 3. CONCLUSIONS: BRC II used the Delphi approach to identify and recommend 31 priorities for surgical education in 2024. We advise establishing a multidisciplinary surgical educational group to oversee, monitor, and facilitate implementation of these recommendations.
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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 | 0.067 | 0.089 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.023 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".