Predictive Factors for Non-Sentinel Lymph Node Metastasis in the Case of Positive Sentinel Lymph Node Metastasis in Two or Fewer Nodes in Breast Cancer
Bibliographic record
Abstract
BACKGROUND: In breast cancer, recent clinical trials have shown that sentinel lymph node biopsy (SLNB) alone without axillary lymph node dissection results in excellent prognosis if there is sentinel lymph node (SLN) metastasis in two or fewer nodes. The aim of the present study was to investigate the association between non-SLN metastasis and clinicopathological factors in case of SLN metastasis in two or fewer nodes in breast cancer. METHODS: Patients who underwent SLNB for invasive breast cancer and were found to have positive SLN in two or fewer nodes were evaluated. The associations between non-SLN metastasis and clinicopahological factors were examined. Statistical analyses were performed using the Mann-Whitney and Chi-square tests, with statistical significance set at P < 0.05. RESULTS: A total of 358 patients were enrolled during the study period and all of these patients were female and 54 patients had SLN metastasis (15%). Positive SLN in two or fewer nodes was identified in 44 patients (81.5%). Among these patients, 17 (38.6%) were found to have non-SLN metastasis. Non-SLN metastasis was associated with invasive tumor size (P = 0.015) and lymphatic involvement (P = 0.035). Multivariate analysis showed that tumor size (P = 0.011) and lymphatic involvement (P = 0.019) remained significant independent predictors of non-SLN metastasis, and that an invasive tumor size cut-off point of 28 mm was useful for dividing patients with positive SLN in two or fewer nodes into non-SLN-positive and non-SLN-negative groups. CONCLUSIONS: Non-SLN metastasis was found in more than 30% of patients with SLN metastasis present in two or fewer nodes. Large tumor size and the presence of lymphatic involvement were significantly associated with non-SLN metastasis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".