Nodal Staging Affects Adjuvant Treatment Choices in Elderly Patients with Clinically Node-Negative, Estrogen Receptor–Positive Breast Cancer
Bibliographic record
Abstract
Background: In response to Choosing Wisely recommendations that sentinel lymph node biopsy (slnb) should not be routinely performed in elderly patients with node-negative (cN0), estrogen receptor–positive (er+) breast cancer, we sought to evaluate how nodal staging affects adjuvant treatment in this population. Methods: From a prospective database, we identified patients 70 or more years of age with cN0 breast cancer treated with surgery for er+ her2-negative invasive disease during 2012–2016. We determined rates of, and factors associated with, nodal positivity (pN+), and compared the use of adjuvant radiation (rt) and systemic therapy by nodal status. Results: Of 364 patients who met the inclusion criteria, 331 (91%) underwent slnb, with 75 (23%) being pN+. Axillary node dissection was performed in 11 patients (3%). On multivariate analysis, tumour size was the only factor associated with pN+ (p = 0.007). Nodal positivity rates were 0%, 13%, 23%, 33%, and 27% for lesions preoperatively sized at 0–0.5 cm, 0.5–1 cm, 1.1–2.0 cm, 2.1–5.0 cm, and more than 5.0 cm. Compared with patients assessed as node-negative, those who were pN+ were more likely to receive axillary rt (lumpectomy: 53% vs. 1%, p < 0.001; mastectomy: 43% vs. 2%, p < 0.001), and adjuvant systemic therapy (endocrine: 82% vs. 69%; chemotherapy plus endocrine: 7% vs. 2%, p = 0.002). Conclusions: Of elderly patients with cN0 er+ breast cancer, 23% were pN+ on slnb. Size was the primary predictor of nodal status, and yet significant rates of nodal positivity were observed even in tumours preoperatively sized at 1 cm or less. The use of rt and systemic adjuvant therapies differed by nodal status, although the long-term oncologic implications require further investigation. Multidisciplinary input on a case-by-case basis should be considered before omission of slnb.
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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".