Bibliographic record
Abstract
OBJECTIVES: Tissue interposition is an important part of vesicovaginal fistula (VVF) repair that has been shown to improve success rates. The most common interpositional flap used during a transabdominal VVF repair is the omental flap; however, in some cases, it cannot be used. The urachus is a well-vascularized tissue that is easily mobilized for interposition. We describe our experience using a urachal flap in VVF repair. METHODS: Patients undergoing VVF repair at our center were identified, and a retrospective chart review was performed. Patients who underwent a transabdominal repair with interposition of a urachal flap were included. RESULTS: Thirteen patients were identified between 2005 and 2009. All were evaluated with a history, physical, upper and lower tract imaging, and cystoscopy. Median patient age was 49 years (range, 31-88 years). Fistula etiology was hysterectomy in 11 and prolapse repair in 2. Five patients presented with recurrent fistulas having failed previous repair. Of 13 patients, 12 had successful repairs with our described technique, including 4 patients who failed previous repairs. There was no recurrence of fistula after median follow-up of 6 months (range, 2 weeks to 4 years). Two patients had preoperative and postoperative complaints of stress urinary incontinence that was mild and did not require surgery. CONCLUSIONS: Vesicovaginal fistulas can be a difficult challenge for the reconstructive surgeon. The urachal flap is a well-vascularized tissue flap that can be easily mobilized and interposed for VVF repair. Of 13 patients in this series, 12 were successfully repaired using this technique. We feel that further evaluation and usage of this tissue flap are indicated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".