Commentary on: Cosmetic Surgery Training in Canadian Plastic Surgery Residencies: Are We Training Competent Surgeons?
Bibliographic record
Abstract
In “Cosmetic Surgery Training in Canadian Plastic Surgery Residencies: Are We Training Competent Surgeons?” Chivers et al discuss the results from a survey of Canadian senior plastic surgery residents assessing their perceptions regarding the quality of their cosmetic surgery training. This mirrors in design our 2 recent publications regarding plastic surgery resident cosmetic training in the United States.1,2 The results and conclusions of these articles are also quite similar. As stated by the authors, major dichotomies face plastic surgery residents and educators in both Canada and the United States: (1) Major advances in plastic surgery over the past 20 years have led to greater levels of sophistication, requiring increased time and effort to gain the necessary knowledge base and technical skills. Yet recent restrictions on resident work hours have reduced training time, so, paradoxically, there are less training hours in the day. (2) Although plastic surgery residencies have increasingly been concentrated in academic centers, the focus of cosmetic surgery has mostly moved outside these centers. This makes the resident cosmetic surgery experience, at times, less than ideal.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.044 | 0.036 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".