Competency-Based Medical Education for Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: North American surgical education is beginning to shift toward competency-based medical education, in which trainees complete their training only when competence has been demonstrated through objective milestones. Pressure is mounting to embrace competency-based medical education because of the perception that it provides more transparent standards and increased public accountability. In response to calls for reform from leading bodies in medical education, competency-based medical education is rapidly becoming the standard in training of physicians. METHODS: The authors summarize the rationale behind the recent shift toward competency-based medical education and creation of the milestones framework. With respect to procedural skills, initial efforts will require the field of plastic surgery to overcome three challenges: identifying competencies (principles and procedures), modeling teaching strategies, and developing assessment tools. The authors provide proposals for how these challenges may be addressed and the educational rationale behind each proposal. RESULTS: A framework for identification of competencies and a stepwise approach toward creation of a principles oriented competency-based medical education curriculum for plastic surgery are presented. An assessment matrix designed to sample resident exposure to core principles and key procedures is proposed, along with suggestions for generating validity evidence for assessment tools. CONCLUSIONS: The ideal curriculum should provide exposure to core principles of plastic surgery while demonstrating competence through performance of index procedures that are most likely to benefit graduating residents when entering independent practice and span all domains of plastic surgery. The authors advocate that exploring the role and potential benefits of competency-based medical education in plastic surgery residency training is timely.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".