Rates of breast cancer surgery in Canada from 2007/08 to 2009/10: retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Surgery is a common and important component of breast cancer treatment. We assessed the rates of breast cancer surgery across Canada from 2007/08 to 2009/10. METHODS: We used hospital and day surgery data from the Canadian Institute for Health Information to assemble a cohort of women who had undergone breast cancer surgery. We identified the index surgical procedure and subsequent surgical procedures performed within 1 year for each woman included in the analysis. We calculated the crude mastectomy rate for each province, and we calculated the adjusted mastectomy rate for select jurisdictions using a logistic regression model fitted using age, neighbourhood income quintile and travel time. RESULTS: In total, 57 840 women underwent breast cancer surgery during the study period. Among women with unilateral invasive breast cancer, the crude mastectomy rate was 39%. Adjusted rates for mastectomy varied widely by province (26%-69%). The rate of re-excision within 1 year for women who had breast-conserving surgery as their index procedure was 23% and varied by province in terms of frequency and type (mastectomy or repeat breast-conserving surgery). Among women who underwent mastectomy for unilateral invasive breast cancer, 6% also underwent contralateral prophylactic mastectomy, and 7% had immediate breast reconstruction following surgery. Of mastectomy procedures, 20% were performed as day surgery; for breast-conserving surgery, 70% were performed as day surgery. INTERPRETATION: There is substantial interprovincial variation in surgical care for breast cancer in Canada. Further research is needed to better understand such variation, and continued monitoring should be the focus of quality initiatives.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".