Practice Patterns in Breast Cancer Surgery: Canadian Perspective
Bibliographic record
Abstract
Breast cancer is a common disease, and the surgical management is continually evolving. The objective of this study was to describe the current breast cancer practice patterns among Canadian surgeons. All active General Surgeons (n=1172), as accredited by the Royal College of Physicians and Surgeons of Canada, were sent a 31-item questionnaire. Anonymous responses were collected and analyzed regarding surgeon demographics, practice, and perceptions regarding surgical care of breast cancer patients. Overall 640 active surgeons responded; of these, 519 (81%) treated breast cancer and formed the study cohort. Practice settings included community (55%), community with university affiliation (28%), and academic (17%). The majority of surgeons (76%) stated that <25% of their practice was devoted to breast disease, and 42% performed < or =2 breast cancer operations/month. Immediate breast reconstruction (IBR) was used by 57% of surgeons. On multivariate analysis, higher surgeon volume of breast cancer cases (p=0.0008), fellowship training in Surgical Oncology (p=0.009), community population (p=0.001), and academic practice setting (p<0.0001) were independently associated with the use of IBR. Of the 640 surgeons who responded, 79% stated that breast cancer surgery should be performed by "most general surgeons." In Canada, most breast cancer surgery was performed by general surgeons who did not appear to have an interest, as defined by training or clinical volume, in breast cancer. Although variability regarding specific surgical issues was found among subgroups of surgeons, the majority of respondents felt that most general surgeons should treat breast cancer.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".