De-Escalating Breast Cancer Surgery: Should We Apply Quality Indicators from Other Jurisdictions in Canada?
Bibliographic record
Abstract
Quality Indicators (QIs), including the breast-conserving surgery (BCS) rate, were published by the European and American Breast Cancer Societies and this study assesses these in a Canadian population to look for opportunities to de-escalate surgery. A total of 2311 patients having surgery for unilateral, unifocal breast cancer between 2013 and 2017 were identified and BCS QIs calculated. Reasons for mastectomy had been prospectively collected with synoptic operative reporting. Our BCS rate for invasive cancer < 3 cm was 77.1%, invasive cancer < 2 cm was 84.1%, and DCIS < 2 cm was 84.9%. There was no statistically significant change in BCS rates over a five-year period, but there was a reduction in contralateral prophylactic mastectomies (CPM) from 28% in 2013 to 16% in 2017 (p < 0.001). Trend analysis looking at tumour size and medical need for mastectomy indicated that 80% of patients at our centre would be eligible for BCS with tumour cut off of 2.5 cm. Our institution met American but not European QI standards for BCS rates, potentially indicating a difference in patient demographics compared to Europe. Our results support the understanding that BCS rates are influenced by multiple factors and are challenging to compare across jurisdictions. CPM rates may offer a more actionable opportunity to de-escalate surgery for 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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".