Robotic sentinel node mapping in clinical stage 1 endometrial cancer using methylene blue dyes using the robotic platform
Bibliographic record
Abstract
PURPOSE: Endometrial cancer is a surgically staged cancer. We examined our preliminary experience with sentinel lymph node (SLN) mapping in early stage endometrial cancer using methylene blue dyes. METHOD: Retrospective review of all clinically stage 1 endometrial cancer staged surgically using the robotic platform. Logistic regression models were built to predict nodal metastasis taking into account age, grade, histology, depth of myometrial invasion, cervical involvement, and use of SLN mapping. RESULTS: Four hundred sixty-nine patients were reviewed. Sixty patients had SLN mapping (13%). Four hundred nine patients underwent standard lymphadenectomy with five documented nodal metastasis (1.2%). Five nodal metastasis (8.3%) were seen in the SLN patients. In the logistic model, the application of SLN mapping was significantly associated with diagnosed nodal metastasis (OR 7.74; 95% CI, 2.04-29.3; P = .003) together with nonendometroid histology (OR 5.05; 95% CI, 1.27-20.12; P = .022). CONCLUSION: SLN mapping protocol using methylene blue significantly identifies more nodal metastasis than standard lymphadenectomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".