Training Oncoplastic Breast Surgeons: The Canadian Fellowship Experience
Bibliographic record
Abstract
BACKGROUND: Oncoplastic breast surgery combines traditional oncologic breast conservation with plastic surgery techniques to achieve improved aesthetic and quality-of-life outcomes without sacrificing oncologic safety. Clinical uptake and training remain limited in the Canadian surgical system. In the present article, we detail the current state of oncoplastic surgery (ops) training in Canada, the United States, and worldwide, as well as the experience of a Canadian clinical fellow in ops. METHODS: The clinical fellow undertook a 9-month audit of breast surgical cases. All cases performed during the fellow's ops fellowship were included. The fellowship ran from October 2015 to June 2016. RESULTS: During the 9 months of the fellowship, 67 mastectomies were completed (30 simple, 17 modified radical, 12 skin-sparing, and 8 nipple-sparing). The fellow participated in 13 breast reconstructions. Of 126 lumpectomies completed, 79 incorporated oncoplastic techniques. CONCLUSIONS: The experience of the most recent ops clinical fellow suggests that Canadian ops training is feasible and achievable. Commentary on the current state of Canadian ops training suggests areas for improvement. Oncoplastic surgery is an important skill for breast surgical oncologists, and access to training should be improved for Canadian surgeons.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".