Vaginal Cuff Dehiscence in Robotic-Assisted Total Hysterectomy
Bibliographic record
Abstract
STUDY OBJECTIVE: The aim of this study was to estimate the cumulative incidence of vaginal cuff dehiscence in robotic-assisted total hysterectomies in our patients and to provide recommendations to decrease the incidence of vaginal cuff dehiscence. METHODS: This was an observational case series, Canadian Task Force Classification II-3 conducted at an academic and community teaching hospital. A total of 654 patients underwent robotic-assisted total laparoscopic hysterectomy for both malignant and benign reasons from September 1, 2006 to March 1, 2011 performed by a single surgeon. The da Vinci Surgical System was used for robotic-assisted total laparoscopic hysterectomy. RESULTS: There were 3 cases of vaginal cuff dehiscence among 654 robotic-assisted total laparoscopic hysterectomies, making our cumulative incidence of vaginal cuff dehiscence 0.4%. The mean time between the procedures and vaginal cuff dehiscence was 44.3 d (6.3 wk). All patients were followed up twice after surgery, at 3 to 4 wk and 12 to 16 wk. CONCLUSION: In our study, the incidence of vaginal cuff dehiscence after robotic-assisted total laparoscopic hysterectomy compares favorably to that of total abdominal and vaginal hysterectomy. Our study suggests that the incidence of vaginal cuff dehiscence is more likely related to the technique of colpotomy and vaginal cuff suturing than to robotic assisted total hysterectomy per se. With proper technique and patient education, our vaginal dehiscence rate has been 0.4%, which is 2.5 to 10 times less than the previously reported vaginal cuff dehiscence rate in the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".