Impact of the Sentinel Node Frozen Section Result on the Probability of Additional Nodal Metastases as Predicted by the MSKCC Nomogram in Breast Cancer
Bibliographic record
Abstract
OBJECTIVE: Sentinel lymph node frozen section is used to obviate the need for a second operation in breast cancer patients with involved nodes. However, the overall sensitivity, specificity and accuracy of sentinel lymph node frozen section are debated, and the impact of sentinel lymph node frozen section positivity on the risk of additional nodal metastases is not known and was the focus of this investigation. METHODS: We used our hospital record system to identify 176 sentinel lymph node biopsies done out of 354 cases of Stage T1-3N0 breast cancers managed from 2005 to 2007 and evaluated the sentinel lymph node frozen section results against the predictions of additional nodal metastases based on the Memorial Sloan-Kettering Breast Cancer Nomogram which is a validated tool for this purpose. RESULTS: Sentinel lymph node metastases size was an independent predictor of sentinel lymph node frozen section sensitivity and those with macrometastases had 15 times the odds (odds ratio, 15.4; 95% confidence interval, 3.4-69.1) of having a true-positive frozen section when compared with those with micrometastases. The breast cancer nomogram predicted that the latter patients have a very low probability of additional nodal metastases with a median probability at 10% (inter-quartile range, 7-14%). CONCLUSIONS: A negative sentinel lymph node frozen section is also associated with a low probability of additional nodal metastases. Additional prognostic factors in the breast cancer nomogram are of little clinical impact because the most predictive factor in the nomogram is the method of detection.
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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 | 0.005 | 0.022 |
| 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 | 0.001 | 0.001 |
| 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".