Cavity Shave Margins in Breast Conservative Surgery a Strategy to Reduce Positive Margins and Surgical Time
Bibliographic record
Abstract
Background: Resection of additional tissue circumferentially around the cavity left by lumpectomy (cavity shave) was suggested to reduce rates of positive margins and re-excision. Methods: A single center retrospective study which analyzed margins status, re-excision, and surgical time in patients who underwent breast conserving surgery and cavity shave or intraoperative evaluation of resection margins. Results: Between 2021 and 2023, 594 patients were enrolled in the study. In patients subjected to cavity shave, a significant reduction in positive, focally positive, or closer margins was reported 8.9% vs. 18.5% (p = 0.003). No difference was reported in terms of surgical re-excision (p < 0.846) (5% vs. 5.5%). Surgical time was lower in patients subjected to cavity shave (<0.001). The multivariate analysis intraoperative evaluation of sentinel lymph node OR 1.816 and cavity shave OR 2.909 were predictive factors for a shorter surgical time. Excluding patients subjected to intraoperative evaluation of sentinel lymph node and patients with ductal carcinoma in situ, patients that underwent the cavity shave presented a reduced surgical time (67.9 + 3.8 min vs. 81.6 + 2.8 min) (p = 0.006). Conclusions: Cavity shaving after lumpectomy reduced the rate of positive margins and it was associated with a significant reduction in surgical time compared to intraoperative evaluation of resection margins.
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 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 | 0.001 |
| 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".