Consensus of Leaders in Plastic Surgery: Identifying Procedural Competencies for Canadian Plastic Surgery Residency Training Using a Modified Delphi Technique
Bibliographic record
Abstract
BACKGROUND: Transitioning to competency-based surgical training will require consensus regarding the scope of plastic surgery and expectations of operative ability for graduating residents. Identifying surgical procedures experts deemed most important in preparing graduates for independent practice (i.e., "core" procedures), and those that are less important or deemed more appropriate for fellowship training (i.e., "noncore" procedures), will focus instructional and assessment efforts. METHODS: Canadian plastic surgery program directors, the Canadian Society of Plastic Surgeons Executive Committee, and peer-nominated experts participated in an online, multiround, modified Delphi consensus exercise. Over three rounds, panelists were asked to sort 288 procedural competencies into five predetermined categories within core and noncore procedures, reflecting increasing expectations of ability. Eighty percent agreement was chosen to indicate consensus. RESULTS: Two hundred eighty-eight procedures spanning 13 domains were identified. Invitations were sent to 49 experts; 37 responded (75.5 percent), and 31 participated (83.8 percent of respondents). Procedures reaching 80 percent consensus increased from 101 (35 percent) during round 1, to 159 (55 percent) in round 2, and to 199 (69 percent) in round 3. The domain "burns" had the highest rate of agreement, whereas "lower extremity" had the lowest agreement. Final consensus categories included 154 core, essential; 23 core, nonessential; three noncore, experience; and 19 noncore, fellowship. CONCLUSIONS: This study provides clarity regarding which procedures plastic surgery experts deem most important for preparing graduates for independent practice. The list represents a snapshot of expert opinion regarding the current training environment. As our specialty grows and changes, this information will need to be periodically revisited.
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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 | 0.085 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".