Non-Sentinel Lymph Node Metastasis is Hard to Predict by Clinicopathological Factors if SLN Metastasis in Two or Fewer Nodes in Breast Cancer
Bibliographic record
Abstract
Background: The aim of this study was to investigate the association between sentinel lymph node (SLN) and/or non-SLN metastasis and clinicopathological factors in breast cancer. Methods: We identified 176 invasive breast cancer patients by SLN biopsy (SLNB) and evaluated any association between clinicopathological factors and SLN and/or non-SLN metastasis. Results: SLN metastasis was significantly associated with age (P = 0.0231), tumor size (P = 0.0039) and lymphovascular involvement (LVI) (P = 0.0002). Non-SLN metastasis was observed in 41.4% of cases. The involvement of more than three nodes was observed in more than 30% of cases with SLN metastasis in two or fewer nodes. There was no significant association between non-SLN metastasis and clinicopathological factors. Conclusions: Non-SLN metastasis was apparent in more than 30% of cases even if SLN metastasis was present in two or fewer nodes but non-SLN metastasis was hard to predict by clinicopathological factors. J Curr Surg. 2014;4(1):10-16 doi: http://dx.doi.org/10.14740/jcs206w
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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 | 0.001 | 0.000 |
| 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".