Vaginal Natural Orifice Transluminal Endoscopic Surgery (vNOTES) for Gynecological Procedures in Obese Patients: A Systematic Review
Bibliographic record
Abstract
Aim: This study was conducted to determine the feasibility, safety, and clinical outcomes of the vaginal natural-orifice transluminal endoscopic surgery (vNOTES) approach in gynecology for obese patients. Methods: PubMed, Cochrane Library, and Google Scholar were searched, from inception to April 2025. A systematic review was performed following the PRISMA guidelines. Studies assessing the use of vNOTES for gynecological procedures in obese women were included. The quality of included articles was evaluated according to the Newcastle–Ottawa Scale. Results: The search yielded three retrospective cohort studies, one cross-sectional, and ten case series. The patients in the vNOTES group (n = 99) had statistically significant shorter operative times, reduced hospitalization, lower postoperative pain scores, fewer perioperative complications, and improved quality of life when compared to the laparoscopy group (n = 84). A study compared obese women to non-obese women undergoing vNOTES and found that operative times were longer in the obese group. Conversion to laparoscopy or laparotomy occurred in fewer than 5% of cases, and intraoperative and postoperative complication rates were low across all studies. Conclusions: vNOTES appears to be safe and potentially superior to other minimally invasive techniques. The small sample size of the case series and the lack of a sufficient number of comparative studies limit the strength of the conclusions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".