Prognostic Significance of the Number of Axillary Lymph Nodes Removed in Patients With Node-Negative Breast Cancer
Bibliographic record
Abstract
PURPOSE: The objective of the study was to evaluate the association between the number of lymph nodes removed at axillary dissection and recurrence and survival for patients with node-negative invasive breast cancer. PATIENTS AND METHODS: Subjects were 2,278 women with pathologically node-negative invasive breast cancer, diagnosed from 1989 to 1993 in British Columbia, Canada. Women aged > or = 90 years, with pure in-situ, bilateral invasive breast cancer or T4, N1, N2, or M1 stage, or who had axillary radiation were excluded. Two groups were defined for analysis: node-negative with no systemic therapy (n = 1,468) and node-negative with systemic therapy (n = 810). Median follow-up was 7.5 years. Prognostic variables assessed were age at diagnosis, tumor size, tumor grade, invasion of lymphatics, veins, or nerves, estrogen receptor status, and number of nodes removed. RESULTS: For patients not receiving systemic therapy, regional relapse was significantly increased with smaller numbers of nodes removed (P =.03). There was a trend toward shorter overall survival with fewer nodes removed (P =.06). Node-negative patients who received systemic therapy did not have a higher regional relapse rate or shorter overall survival when fewer nodes were recovered. CONCLUSION: Recovery of a small number of negative lymph nodes at axillary dissection likely understages patients and leads to undertreatment, resulting in an increased regional relapse rate and poorer survival. The use of systemic therapy may overcome this effect. The number of nodes removed, in conjunction with other prognostic factors, may be useful in selecting node-negative patients for systemic therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".