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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".