Axillary Management for Patients Undergoing Total Mastectomy and a Positive Sentinel Lymph Node: Is Axillary Dissection Necessary?
Bibliographic record
Abstract
PURPOSE: We sought to evaluate whether patients with breast cancer who undergo a total mastectomy (TM) can safely forgo a completion axillary lymph node dissection (cALND) in the presence of one to three positive sentinel lymph nodes (SLN+). METHODS: A multicenter retrospective cohort study (2012-2022) was conducted in patients with cT1-3cN0 who underwent TM with 1-3 SLN+ compared by cALND versus. no further surgery. We compared overall survival (OS) and locoregional recurrence rates (LRR) and investigated whether the omission of cALND altered adjuvant treatment. RESULTS: In total, the study included 139 patients with SLN+TM, with a mean tumor size of 19.44 mm (SD:10.64); 76% (n = 105) of these patients underwent SLNB-alone. Patients treated by cALND had a younger mean age than those treated by SLNB-alone (49.5 vs. 56 years and p = 0.016). Patients undergoing cALND were more likely to have macrometastatic disease (97% vs. 65% and p < 0.001) and extranodal extension (47% vs. 29% and p = 0.046). cALND was associated with higher rates of adjuvant chemotherapy (88% vs. 62% and p = 0.004). Postmastectomy radiotherapy (PMRT) was similar between groups (79% vs. 82% and p = 0.68). At a mean follow-up of 5.2 years, there was one chest-wall LRR in the SLNB group, with no axillary recurrences. LRR did not significantly differ with or without cALND (2.9% vs. 1.0% and p = 0.4). Five-year overall survival rates were similar between groups (100% vs. 94% and p = 0.2). CONCLUSION: We found high OS and low LRR among patients undergoing upfront TM with 1-3 SLN+ without cALND. Completion ALND did not decrease receipt of PMRT but was associated with higher rates of adjuvant chemotherapy. Our findings support the omission of cALND after TM for patients with 1-3 SLN+.
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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 | 0.003 |
| 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".