419 Survival after minimally invasive surgery in early cervical cancer: is the uterine manipulator to blame?
Bibliographic record
Abstract
Objectives Minimally invasive radical hysterectomy (MIS-RH) has been associated with decreased survival in patients with early cervical cancer. The objective of this study was to determine whether the use of an intrauterine manipulator at the time of laparoscopic or robotic radical hysterectomy (RH) impacts patient outcomes. Methods Retrospective study of all patients who underwent treatment of cervical cancer by MIS-RH at two large volume centres between 2006 and 2018. Results A total of 224 patients were identified at the 2 centres; 115 had surgery with the use of an intrauterine manipulator, while 109 did not. Patients in whom a uterine manipulator was not used were more likely to have residual disease at hysterectomy (p<0.0001), positive lymphovascular space invasion (LVSI) (p=0.02), positive margins (0.0081), and positive lymph node metastasis (0.0029). Recurrence free survival (RFS) at 5 years was 80% in the no manipulator group and 94% in the manipulator group. After controlling for the presence of residual cancer at hysterectomy, tumor size (microscopic <7 mm or macroscopic ≥7 mm) and high-risk pathologic criteria (positive margins, parametria or lymph nodes), the use of a uterine manipulator was no longer significantly associated with RFS (HR=0.49, p=0.12). The only factor which was consistently associated with RFS was tumor size ≥7 mm (HR=9.5, p=0.03). Conclusion The use of a uterine manipulator in patients with early cervical cancer treated with MIS-RH was not significantly associated with patients’ risk of recurrence. We identified that the most significant predictor of cancer recurrence in this population was having a macroscopic tumor.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".