Robotic sentinel node mapping in clinical stage 1 endometrial cancer using methylene blue dyes using the robotic platform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it