What is the Burden of Axillary Disease after Neoadjuvant Therapy in Women with Locally Advanced Breast Cancer?
Bibliographic record
Abstract
BACKGROUND: The burden of axillary disease in patients with locally advanced breast cancer (labc) after neoadjuvant therapy (nat) has not been extensively described in a large modern cohort. Here, we describe the extent of nodal metastases after nat in patients with labc. METHODS: All patients with labc treated at a single institution during 2002-2007 were identified. Demographic, radiologic, and pathologic variables were extracted. To assess the extent of lymph node metastases after nat, patients were separated into two groups: those with and without clinical or radiologic evidence of lymph node metastases before nat. Axillary lymph nodes retrieved at surgery that had no evidence of metastases after hematoxylin and eosin (h&e) staining underwent further pathology evaluation. RESULTS: Of the 116 patients identified, 115 were female (median age: 48.5). Before nat, 26 patients were clinically and radiologically node-negative; of those 26, 14 were histologically negative on final pathology. After serial sectioning and immunohistochemistry, 9 of 26 (35%) were node-negative. Of the 90 patients who had clinical or radiologic evidence of lymph node metastases before nat, 23 (26%) had no evidence of lymph node metastases on h&e staining. After serial sectioning and immunohistochemistry, 19 (21%) had no further axillary lymph node metastases. Overall, 76% of patients had pathology evidence of lymph node metastases after nat. CONCLUSIONS: Most patients with labc have axillary metastases after nat. Our findings support axillary lymph node dissection and locoregional radiation in most patients with labc after nat.
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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 | 0.001 | 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".