The Impact of Intrauterine Manipulators on Outcome and Recurrence Patterns of Endometrial Cancer Patients Undergoing Minimally Invasive Surgery
Bibliographic record
Abstract
Objective: To evaluate the use of manipulators on the outcome of women who had minimally invasive surgery for endometrial cancer. Methods: Retrospective analysis of patients operated with or without an intrauterine manipulator. Results: Six hundred ninety-nine patients were included. The median follow-up was 44 months (range, 29–67). Nineteen (8.8%) patients had positive cytology in the manipulator group versus 21 (4.4%) in the comparison group ( p = 0.02). Total recurrence rate was similar between the groups (12.3% vs. 11.9%; p = 0.8). Vaginal vault recurrence was the most common site of recurrence with higher incidence in the manipulator group (4.5% vs. 1.3%; p = 0.007). Subgroup analysis of low-risk patients who did not receive adjuvant treatment showed higher recurrence rate (8.3% vs. 3%; p = 0.023) and worse disease-free survival ( p = 0.01) for the manipulator group. After controlling for other variables, the use of a manipulator did not affect the risk of recurrence for the whole cohort (hazard ratio [HR], 1.28; confidence interval [95% CI], 0.7–2.1, p = 0.3) and for the low-risk subgroup of patients who did not receive adjuvant treatment (HR, 2.47; 95% CI, 0.8–7, p = 0.08). Conclusion: The use of a manipulator increases the risk of positive cytology as well as vaginal vault recurrences, but it does not reduce the overall survival of patients.
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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 | 0.000 | 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".