Incidence of and Risk Factors for Vaginal Cuff Dehiscence After Hysterectomy: A 10-Year Retrospective Case Control Study at Two Institutions
Bibliographic record
Abstract
Objective: The goal of this research was to determine the incidence of vaginal cuff dehiscence after all types of total hysterectomies and to identify potential risk factors for dehiscence. Design: A retrospective chart review was performed to identify the incidence of vaginal cuff dehiscence after hysterectomy. This review was followed by a case-control study to identify risk factors for dehiscence (Canadian Task Force classification II-2). Materials and Methods: A retrospective chart review was performed on all women, at two institutions, who experienced vaginal cuff dehiscence after total hysterectomy over a 10-year period. All patients who experienced cuff dehiscence after any type of hysterectomy were included to determine the incidence of vaginal cuff dehiscence. A case-control study was then performed to identify risk factors for dehiscence. Results: Between 2000 and 2010 there were 4235 total hysterectomies performed and 8 dehiscences giving an overall incidence of vaginal cuff dehiscence of 0.19%. There were the following dehiscences: 1 after total laparoscopic hysterectomy (TLH; incidence: 0.59%, 95% confidence interval [CI]: −0.5% to 1.7%); 1 after laparoscopically assisted vaginal hysterectomy (incidence: 0.19%, 95% CI: −0.19 to 0.56%); 1 after robotic-assisted TLH (incidence: 0.52%, 95% CI: −0.50 to 1.6%); 5 after total abdominal hysterectomy (incidence; 0.19%; 95% CI: −0.02–0.36%); and 0 after vaginal hysterectomy. There was no significant increase in risk detected in this study when examining mode of hysterectomy as a risk factor for dehiscence. Conclusions: The reported incidence of vaginal cuff dehiscence after laparoscopic and robotic hysterectomy may be lower than had been reported previously. (J GYNECOL SURG 30:276)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".