Neoadjuvant Chemotherapy in Invasive Breast Cancer Results in a Lower Axillary Lymph Node Count
Bibliographic record
Abstract
BACKGROUND: It is essential to have the highest level of confidence in axillary staging assessment. Many surgeons and pathologists believe that fewer lymph nodes are present in axillary dissection specimens of women treated by neoadjuvant chemotherapy. Consequently, the purpose of this study was to compare the lymph node counts of axillary dissection specimens from patients having received neoadjuvant chemotherapy with those of patients treated with primary operation. STUDY DESIGN: A retrospective analysis of a prospective database from our institution identified 283 women with invasive breast cancer who underwent level I and II axillary lymph node dissections. Women from the neoadjuvant chemotherapy group (n=107) were compared with those from the primary surgery group (n=176). The total number of lymph nodes harvested was considered as a continuous variable, but also dichotomized into two categories (< 10 and >or=10). Its correlation with the different variables was analyzed. RESULTS: The median number of lymph nodes retrieved in the neoadjuvant chemotherapy group was 10.0 (range 0 to 38) compared with 12.5 (range 0 to 30) in the control group (p=0.002). There were also significantly more patients with fewer than 10 lymph nodes recovered in the neoadjuvant group (45 versus 28%, p=0.007). Logistic regression showed that neoadjuvant chemotherapy was the only factor associated with retrieval of fewer than 10 lymph nodes. CONCLUSIONS: This study suggests that administration of neoadjuvant chemotherapy to breast cancer patients results in a reduced number of lymph nodes retrieved in the axillary dissection specimens.
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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 | 0.003 |
| 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".