Nipple margin assessment at the time of nipple-sparing mastectomy
Bibliographic record
Abstract
BACKGROUND: Documenting negative margins at the nipple-areolar complex (NAC) during nipple-sparing mastectomy (NSM) remains the standard, but how to achieve this and how to manage a positive margin is debated. We sought to review nipple margin assessments at our institution and to analyze the risk factors of a positive margin and rate of local recurrence. METHODS: Patients who underwent NSM between 2012 and 2018 were reviewed and divided into 3 groups based on indication - cancer, contralateral prophylactic mastectomy (CPM) and bilateral prophylactic mastectomy (BPM). RESULTS: Nipple-sparing mastectomies were performed on 337 patients; 72% for cancer, 20% for CPMs and 8% for BPMs. Nipple margin assessments were performed in 87.8% of patients; 10 patients (3.4%) had a positive margin, 7 of whom underwent NAC excision and 3 were managed with observation. CONCLUSION: As indications for NSM increase, assessment of nipple margin provides valuable information to manage the NAC in patients with cancer. The routine use of nipple margin biopsies in patients undergoing CPM and BPM may no longer be required, as rates of occult malignant disease are low with no positive biopsies. Further studies with larger sample sizes are needed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".