The Development of Assessment Tools for Plastic Surgery Competencies
Bibliographic record
Abstract
BACKGROUND: Objective tools to assess procedural skills in plastic surgery residency training are currently lacking. There is an increasing need to address this deficit in order to meet today's training standards in North America. OBJECTIVES: The purpose of this pilot study was to establish a methodology for determining the essential procedural steps for two plastic surgery procedures to assist resident training and assessment. METHODS: Following a literature review and needs assessment of resident training, the authors purposefully selected two procedures lacking robust assessment metrics (breast augmentation and facelift) and used a consensus process to complete a list of procedural steps for each. Using an online survey, plastic surgery Program Directors, Division Chiefs, and the Royal College Specialty Training Committee members in Canada were asked to indicate whether each step was considered essential or non-essential when assessing competence among graduating plastic surgery trainees. The Delphi methodology was used to obtain consensus among the panel. Panelist reliability was measured using Cronbach's alpha. RESULTS: A total of 17 steps for breast augmentation and 24 steps for facelift were deemed essential by consensus (Cronbach's alpha 0.87 and 0.85, respectively). CONCLUSION: Using the aforementioned technique, the essential procedural steps for two plastic surgery procedures were determined. Further work is required to develop assessment instruments based on these steps and to gather validity evidence in support of their use in surgical 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 | 0.051 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".