Current Practices and Trends of Plastic and Oncoplastic Breast Surgeons in Canada
Bibliographic record
Abstract
Background: There is a lack of previous studies investigating oncoplastic practice trends for breast reconstruction in Canada, particularly from the plastic surgeon perspective. Given the rising popularity of oncoplastic techniques, this study aimed to identify current practice trends for breast and plastic surgeons in Canada. Methods: A cross-sectional survey study of breast and plastic surgeons performing oncoplastic surgery across Canada was conducted. Results: Ninety-five surgeons were invited to complete the survey, with 58 respondents (response rate 61%), of which 29 (50.0%) were breast surgeons and 29 (50.0%) were plastic surgeons. Compared to plastic surgeons, breast surgeons performed significantly more level 1 surgeries (27.6 vs 3.45%, P < .001). Plastic surgeons performed more level 2 (37.9% vs 13.8%, P = .0475) and level 3 (31.4% vs 10.3%, P = .00814) surgeries. Breast surgeons identified significant perceived barriers including unfamiliarity with techniques ( P = .00513), adjuvant therapy delays ( P = .00612), lack of plastic surgery support ( P < .001), lack of radiation oncology support ( P = .0485), increased OR time ( P < .001), lack of OHIP billing codes ( P < .001), and post-operative complication management ( P = .0372). Breast surgeon comfort with oncoplastic techniques was not correlated with practice duration (R-square = .037, P -value = .853). Breast surgeon comfort with contralateral surgery was not correlated with practice setting (R-square = .071, P -value = .632). Conclusions: Breast surgeons perceive a lack of training, a lack of support from plastic surgery, concerns regarding appropriate financial remuneration, and worries of increased OR time as barriers in oncoplastic surgery. Collaboration between general breast surgery and plastic surgery is needed for improving training options for oncoplastic surgery in Canada and for providing excellent breast cancer care overall.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".