Key Issues in Surgical Residency Education
Bibliographic record
Abstract
BACKGROUND: In September 2022, a summit was convened by the American Board of Surgery (ABS) to discuss competency-based reform in surgical education. A key output of that summit was the recommendation that the prior work of the Blue Ribbon I Committee convened 20 years earlier be revived. With leadership from the American College of Surgeons (ACS) and the American Surgical Association (ASA), the Blue Ribbon Committee (BRC) II was subsequently convened. This paper describes the output of the Residency Education Subcommittee of the BRC II Committee. METHODS: The Subcommittee organized its work around prioritized themes, including curriculum, assessment, and transition to practice. Top recommendations, time-based action steps, potential barriers, and required resources were detailed and vetted through group discussion, broader Committee review and critique, and subsequent refinement. RESULTS: Primary concluding emphases included transitioning to a competency-based training model, facilitating dynamically capable curricular reform emphasizing the digital transformation of surgical care, using predictive analytic assessment strategies to optimize training effectiveness and efficiency, and creating mentorship strategies to govern the transition from training to independent practice in an outcomes-accountable fashion. CONCLUSIONS: To implement the recommendations outlined, it was recognized that coordinated efforts across existing organizational structures will be required, informed by data set integration strategies that meaningfully measure educational and related patient outcomes.
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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 | 0.054 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".