Prediction of involvement of sentinel and nonsentinel lymph nodes in a Canadian population with breast cancer.
Bibliographic record
Abstract
BACKGROUND: We sought to identify criteria for the intraoperative assessment of sentinel lymph node (SLN) involvement in women with early breast cancer. We also sought to determine whether the SLN nomogram developed by the Memorial Sloan-Kettering Cancer Center (MSKCC) to predict nonsentinel lymph node (NSLN) involvement when the SLN is positive would accurately predict NSLN involvement in our patient population. METHODS: We performed 405 SLN biopsies in 397 women between January 1998 and June 2005. We determined factors associated with SLN metastases using univariate and multivariate logistic regression. Ninety women who had 1 positive SLN or more and underwent axillary lymph node dissection (ALND) had complete data for analysis. We applied the MSKCC nomogram retrospectively to this subset of women, and we calculated the probability of NSLN involvement and compared it with the observed rate. RESULTS: Multifocality and the presence of lymphovascular invasion were predictive of SLN involvement. Ductal carcinoma in situ was negatively associated with SLN involvement. Intraoperative evaluation identified 57 (63%) of the 90 women with involved SLN, of which 26 (29%) had involved NSLN. Application of the MSKCC nomogram to our data set produced an area under the receiver operator characteristic curve of 0.71. The nomogram tended to overestimate the probability of NSLN involvement in our population. CONCLUSION: Lymphovascular invasion and multifocality were associated with SLN involvement. Women with small low-grade tumours may not require routine intraoperative evaluation of SLNs. The MSKCC nomogram appears to be most useful as a decision aid in selecting those women with an involved SLN in whom ALND may be omitted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".