Association between extent of axillary lymph node dissection and patient, tumor, surgeon, and hospital factors in patients with early breast cancer
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Axillary lymph node dissection (ALND) in patients with breast cancer is crucial for accurate staging, provides excellent regional tumor control, and is included in the standard of care for the surgical treatment of breast cancer. However, the extent of ALND varies, and the extent of dissection and the number of lymph nodes that comprise an optimal axillary dissection are under debate. Despite conflicting evidence, several studies have shown that improved survival is correlated with more lymph nodes removed in both node-negative and node-positive patients. The purpose of this study is to determine which patient, tumor, surgeon, and hospital characteristics are associated with the number of nodes excised in early breast cancer patients. METHODS: A random sample of 938 women with node-negative breast cancer was drawn from the Ontario Cancer Registry and the data supplemented with chart reviews. The extent of axillary dissection was studied by examining the number of nodes examined in relation to the patient, tumor, surgeon, and hospital factors. RESULTS: The mean number of lymph nodes excised was 9.8 (SD = 4.8; range, 1-31), and 49% of patients had >/=10 nodes excised. Lower patient age was associated with the excision of more lymph nodes (>/=10 nodes: 63% of patients <40 years vs. 38% of patients >/=80 years). Surgeon academic affiliation and surgery in a teaching hospital were highly correlated with each other and were significantly associated with the excision of >/=10 nodes. The number of nodes excised was not associated with any tumor factors, nor with the breast operation performed. These results were confirmed with multivariable models. CONCLUSIONS: Even though the number of lymph nodes found in the pathologic specimen can be influenced by factors other than surgical technique (e.g., number of nodes present, specimen handling, and pathologic examination), this study shows significant variation of this variable and an association with several patient and surgeon/hospital factors. This variation and the association with survival warrant further study and effort at greater consistency.
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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 | 0.000 |
| 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) | 0.000 | 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".