Are Uterine Manipulators Harmful in Minimally Invasive Endometrial Cancer Surgery? A Retrospective Cohort Study
Bibliographic record
Abstract
Objective: The objective of our study was to assess the oncological safety of uterine manipulators (UMs) in apparent early-stage (FIGO I-II 2009) endometrial cancer treated by minimally invasive surgery (MIS). Methods: Our single-center retrospective study includes patients who underwent endometrial cancer surgery for apparent early-stage disease by either laparoscopy or by robotic or laparoscopic-assisted vaginal hysterectomy from November 2012 to December 2020. Data on UMs, isolated tumor cells (ITCs), cytology, lymphovascular space invasion, free cancer cells in fallopian tubes, stage, histology and grade were collected. Primary and secondary outcomes were cancer recurrence and disease-specific death. Kaplan–Meier curves and multivariate logistic regression were used for statistical analysis. Results: A total of 930 women with early-stage endometrial cancer were included; 789 (84.8%) had hysterectomy with a uterine manipulator and 141 (15.2%) without. A total of 88% had endometrioid histology, 71.6% were grade 1 and 95.7% had stage I disease. A higher risk of recurrence was observed with the Hohl manipulator (HR: 2.83. 95% CI: 1.004–7.98 p = 0.0492) on univariate analysis. On multivariate analysis, neither UM was associated with recurrence. With a mean follow-up of 48 months (range 3–118), no effect was seen on disease-specific death in either Hohl or V-Care (HR: 1.66. 95% CI: 0.48–5.70 and HR:1.29. 95% CI: 0.33–4.98). In high-grade histologies, UMs were strongly associated with recurrence (HR: 12.1. 95% CI: 1.52–96.6 p = 0.019) and disease-specific death (HR: 10.2. 95% CI: 1.12–92.1 p = 0.032). Conclusions: The use of UMs in MIS for endometrial cancer was associated with higher rates of recurrence without affecting disease-specific death, except in high-grade histologies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".