Predictors of residual disease after breast conservation surgery for ductal carcinoma in situ: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Breast-conserving therapy is the standard of care for ductal carcinoma in situ (DCIS). Debate on what constitutes a satisfactory margin persists. This study aimed to identify predictors of residual disease at re-excision. METHODS: This is a population-based retrospective cohort study of women with DCIS who underwent a lumpectomy between 2007 and 2017 in Manitoba, with close (≤2 mm) or positive margins that led to re-excision. RESULTS: The DCIS re-excision rate was 29.3% for 1001 patients. 63.2% of patients were found to have residual disease on re-excision. On univariable analysis, the size, margin status, number of positive margins, type of second surgery, and Van Nuys Prognostic Index score were associated with residual disease on re-excision. The size of DCIS and the number of positive margins remained statistically significant on multivariable analysis. CONCLUSIONS: Re-excision should be rationalized by considering the predictors of residual disease in conjunction with other factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".