Re-excision rates after breast-conserving surgery for invasive breast cancer: an Albertan perspective
Bibliographic record
Abstract
BACKGROUND: Re-operation after breast-conserving surgery for invasive breast cancer is variable among centres and individual surgeons. In this study, we aimed to characterize the current landscape of practice regarding re-operation for invasive breast cancer in the province of Alberta. METHODS: This study was a retrospective review of the Synoptec database for patients undergoing primary breast-conserving surgery for invasive breast cancer or reoperation in the province of Alberta in the year 2020. We extracted data on demographic and tumour characteristics, use of intraoperative margin-assessment strategies, and surgical facility. We conducted univariate and multivariate logistic model analyses. RESULTS: We included 1391 breast surgeries in the study. A total of 158 patients underwent re-operation during the study period. The median time to first reoperation was 34 days. The overall re-operation rate was 11.4% (range 5.4%-18.5%) among surgical facilities. The completion mastectomy rate was 5.2%, and 1.5% of patients underwent multiple revisional surgeries. Tumour multifocality was associated with increased revisional surgery rates on multivariate analysis (odds ratio 2.80). CONCLUSION: The results of this study are consistent with the published literature. We have identified heterogeneity among sites in Alberta for revisional surgery after breast-conserving surgery for invasive breast cancer. This highlights an opportunity for ongoing education and quality improvement in breast cancer care in the province of Alberta.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".