How Do Pathologists in Academic Institutions Across the United States and Canada Evaluate Sentinel Lymph Nodes in Breast Cancer? A Practice Survey
Bibliographic record
Abstract
OBJECTIVES: There are little data on how changes in the clinical management of axillary lymph nodes in breast cancer have influenced pathologist evaluation of sentinel lymph nodes. METHODS: A 14-question survey was sent to Canadian and US breast pathologists at academic institutions (AIs). RESULTS: Pathologists from 23 AIs responded. Intraoperative evaluation (IOE) is performed for selected cases in 9 AIs, for almost all in 10, and not performed in 4. Thirteen use frozen sections (FSs) alone. During IOE, perinodal fat is completely trimmed in 8, not trimmed in 9, and variable in 2. For FS, in 12 the entire node is submitted at 2-mm intervals. Preferred plane of sectioning is parallel to the long axis in 8 and perpendicular in 12. In 11, a single H&E slide is obtained, whereas 12 opt for multiple levels. In 11, cytokeratin is obtained if necessary, and immunostains are routine in 10. Thirteen consider tumor cells in pericapsular lymphatics as lymphovascular invasion (LVI), and 10 consider it isolated tumor cells (ITCs). CONCLUSIONS: There is dichotomy in practice with near-equal support for routine vs case-by-case multilevel/immunostain evaluation, perpendicular vs parallel sectioning, complete vs incomplete fat removal, and tumor in pericapsular lymphatics as LVI vs ITCs.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".