Unidirectional Barbed Suture for Vaginal-Cuff Closure in Laparoscopic and Robotic Hysterectomy
Bibliographic record
Abstract
Objective: The aim of this research was to estimate the incidence of vaginal-cuff dehiscence in patients undergoing total laparoscopic hysterectomy (TLH) or TLH with robotic assistance, using a unidirectional barbed suture compared to a vicryl suture. Materials and Methods: For this retrospective cohort study (Canadian Task Force Classification II-3), a total of 474 patient records were reviewed for women who underwent either TLH or TLH with robotic assistance. The procedures were performed by experienced minimally invasive gynecologic surgeons at a tertiary-care university-based teaching hospital and a community hospital from July 2010 to November 2012. Results: The overall incidence of vaginal-cuff dehiscence was 0.6%. There were 3 patients in the unidirectional barbed suture group diagnosed with vaginal cuff dehiscence, (0.8%, 95% CI: 0.2 to 2.4%), compared with no vaginal-cuff dehiscence in the delayed absorbable suture group (0.0%, 95% CI: 0.0% to 3.2%). One patient had recurrent dehiscence. There were no significant differences in postoperative bleeding, cellulitis, granulation tissue, or overall complications between the unidirectional and conventional suture groups (2.7% versus 1%; p = 0.47). Conclusions: Use of a unidirectional barbed suture for vaginal-cuff closure during TLH or TLH with robotic assistance does not increase the risk of vaginal-cuff dehiscence. (J GYNECOL SURG 32:167)
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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 | 0.006 |
| 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".