Assessing para‐aortic nodal status in high‐grade endometrial cancer patients with negative pelvic sentinel lymph node biopsy
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of pelvic sentinel lymph node biopsy (SLN) in detecting positive para-aortic (PA) lymph nodes in high-grade uterine cancer, and to determine the recurrence rate in patients with high-grade uterine cancers who did not receive adjuvant chemotherapy based on negative pelvic SLNs. METHODS: This was a retrospective cohort study of patients with newly diagnosed, high-grade endometrial cancer who underwent surgery, including pelvic SLNs with or without PA node dissection, at a tertiary care institution between 2015 and 2020. Baseline demographics, surgical management, pathology data, and outcomes were analyzed using descriptive statistics, and survival analysis. RESULTS: Postoperative histology of the 110 patients meeting inclusion criteria was 45.5% grade 3 endometrioid, 36.4% serous, 10.9% clear cell, and 7.3% carcinosarcoma. On final pathology, 63.7% were stage 1, and 23.6% were stage 3C with positive nodes. A total of 63 patients (57.3%) had a PA lymph node dissection (56 bilateral, 7 unilateral) in addition to the pelvic SLN. Among this group, 5.8% (95% confidence interval 1.2%-16.0%) had a positive PA node despite a negative pelvic SLN. Among those with a negative pelvic SLN and no adjuvant chemotherapy (n = 75), the rate of distant recurrence was 14.7%, and 3-year recurrence-free survival was 71.9%. CONCLUSION: The rate of isolated PA node metastasis in high-grade endometrial cancers despite a negative pelvic SLN may be significantly higher than the accepted rate of isolated PA node metastasis in low-grade endometrial cancer. This supports adjuvant treatment decisions continuing to incorporate primary tumor pathology and molecular classification.
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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 | 0.000 |
| 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) | 0.001 | 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".