Resident Exposure to Aesthetic Surgical and Nonsurgical Procedures During Canadian Residency Program Training
Bibliographic record
Abstract
North American residency programs are transitioning to competency-based medical education (CBME) to standardize training programs, and to ensure competency of residents upon graduation. At the centre of assessment in CBME are specific surgical procedures, or procedural competencies, that trainees must be able to perform. A study previously defined 31 procedural competencies for aesthetic surgery. In this transition period, understanding current educational trends in resident exposure to these aesthetic procedures is necessary. The aim of this study was to characterize aesthetic procedures performed by Canadian plastic surgery residents during training, as well as to describe resident performance confidence levels and degree of resident involvement during those procedures. Case logs were retrieved from all 10 English-language plastic surgery programs. All aesthetic procedures were identified, and coded according to previously defined core procedural competencies (CPCs) in the aesthetic domain of plastic surgery. Data extracted from each log included the procedure, training program, resident academic year, resident procedural role, and personal competence. From July 2004 to June 2014, 6113 aesthetic procedures were logged by 55 graduating residents. Breast augmentation, mastopexy, and abdominoplasty were the most commonly performed CPCs, and residents report high levels of competence and surgical role in these procedures. Facial procedures, in particular rhinoplasty, as well as nonsurgical CPCs are associated with low exposure and personal competence levels. Canadian plastic surgery residents are exposed to most of the core aesthetic procedural competencies, but the range of procedures performed is variable. With the implementation of CBME, consideration should be given to supplementation where gaps may exist in aesthetic case exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".