Factors Influencing Lymph Node Positivity in HER2/neu+ Breast Cancer Patients
Bibliographic record
Abstract
Axillary lymph node metastases are a key prognostic factor in breast cancer treatment. Our aim was to evaluate how tumor size, tumor location, and imaging results correlate to axillary lymph node diseases for patients with stage I-III HER2/neu+ breast cancer. This is a single-institution retrospective chart review of female breast cancer patients diagnosed with primary invasive Her2/neu+ breast cancer who were treated with upfront surgical resection from 2000–2021. Of 75 cases, 44/75 (58.7%) had nodal metastasis, and there was a significant association of larger tumor size to nodal metastases (p ≤ 0.001). Patients with negative nodes had a smaller mean tumor size (n = 30; 15.10 mm) than patients with positive nodes (n = 45; 23.9 mm) (p = 0.002). Preoperative imaging detected suspicious nodes in 36 patients, and ultrasound detected the most positive nodes (14/18; p = 0.027). Our data confirms that tumor size at diagnosis is correlated with a higher likelihood of axillary involvement in patients with Her2/neu+ breast cancer; notably, a large proportion of Her2/neu+ breast cancers have metastatic involvement of axillary lymph nodes even with small primary lesions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".