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 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) | 0.001 | 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".