Association of Surgical Margin Status with Oncologic Outcome in Patients Treated with Breast-Conserving Surgery
Bibliographic record
Abstract
We aimed to compare the prognosis of patients with close resection margins after breast-conserving surgery (BCS) with that of patients with negative margins and identified predictors of residual disease. A total of 542 patients with breast cancer who underwent BCS between 2003 and 2019 were selected and divided into the close margin (114 patients) and negative margin (428 patients) groups. The median follow-up period was 72 (interquartile range, 42–113) months. Most patients received radiation therapy (RTx) and systemic therapy according to their stage and molecular subtype. The 10-year locoregional recurrence-free survival rates of the close and negative margin groups were 88.2% and 95.5%, respectively (p = 0.001). Multivariable analysis showed that adjuvant RTx and margin status after definitive surgery were significantly associated with locoregional recurrence. Of the 57 patients who underwent re-excision, 34 (59.6%) had residual disease. Multivariable analysis revealed that a histological type of positive or close margins and multifocality were independent predictive factors for residual disease. Although the current guidelines suggest that no ink on tumor is an adequate margin after BCS, a close resection margin may be associated with locoregional failure. The treatment strategy for close resection margins after BCS should be based on individual clinicopathological features.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".