Practice patterns and perceptions of margin status for breast conserving surgery for breast carcinoma: National Survey of Canadian General Surgeons
Bibliographic record
Abstract
BACKGROUND: We surveyed Canadian General Surgeons to examine decision-making in early stage breast cancer. METHODS: A modified Dillman Method was used for this mail survey of 1443 surgeons. Practice patterns and factors that influence management choices for: preoperative assessment, definition of margin status, surgical techniques and recommendations for re-excision were assessed. RESULTS: The response rate was 51% with 41% treating breast cancer. Most (80%) were community surgeons, with equal distribution of low/medium/high volume and years of practice categories. Approximately 25% of surgeons "sometimes or frequently" performed diagnostic excisional biopsies while 90% report "frequently" or "always" performing preoperative core biopsies. There was marked variation in defining negative and close margins, in the use of intra-operative margin assessment techniques and recommendations for re-excision. CONCLUSIONS: Responses revealed significant variation in attitudes and practices. These findings likely reflect an absence of consensus in the literature and potential gaps between best evidence and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".