Does intra‐operative margin assessment improve margin status and re‐excision rates? A population‐based analysis of outcomes in breast‐conserving surgery for ductal carcinoma in situ
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Using a 2 mm margin criteria, we evaluated the effect of intra-operative margin assessment on margin status and re-excisions following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS). METHODS: We identified patients undergoing BCS for DCIS from a prospective, population-based database. Multivariable logistic regression was used to determine the effect of specimen mammography, ultrasound and macroscopic assessment by a pathologist on margins and re-excision rates. RESULTS: In 588 patients, 52% (95% confidence interval [CI], 48%-56%) had positive margins (<2 mm), 39% (95% CI, 35%-43%) had a re-excision and 15% (95% CI, 12%-18%) had completion mastectomy. There were few re-excisions for margins ≥2 mm (2%). Adjusting for confounders, any margin assessment versus wire localization alone did not reduce positive margins (odds ratio [OR], 0.75; P = 0.202) or re-excisions (OR, 1.14; P = 0.564), however both outcomes varied by type of technique ( P < 0.001). Individually, only macroscopic assessment by pathologist reduced positive margins (OR, 0.54; P = 0.002) and re-excisions (OR, 0.61; P = 0.036). CONCLUSIONS: Despite adherence to a 2 mm margin criteria, re-excision rates remain high following BCS for DCIS, with 39% converted to mastectomy when re-excision is required. Intra-operative margin assessment does not appear to reduce re-excisions; in particular, surgeons should be aware of the limitations of specimen mammography for margin assessment in DCIS.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".