Causes and management of urogenital fistulas
Bibliographic record
Abstract
OBJECTIVES: To reviewe the etiology and management of urogenital fistulas at a tertiary care referral center. METHODS: We retrospectively identified all patients with urogenital fistula referred to the King Fahad Medical City, Riyadh, Saudi Arabia, between January 2005 and July 2016 from electronic records. We collected data on age, parity, etiology and type of fistula, radiologic findings, management, and outcome. Results: Of the 32 patients with urogenital fistula identified, 17 (53.1%) had vesicovaginal fistula. The mean parity was 5.9 (0-15). Obstetric surgery was the most common etiology, accounting for 22 fistulas (68.8%). Twenty of these (90.9%) were complications of cesarean delivery, of which 16 (80%) were repeat cesarean delivery. Forty surgical repair procedures were performed: 20 (50%) via an abdominal approach, 11 (27.5%) via a vaginal approach, 7 (17.5) via a robotic approach, and 2 (5%) using cystoscopic fulguration. The primary surgical repair was successful in 23 patients (74%), the second repair in 5 (16.1%), and the third repair in one (3.1%). One fistula was cured after bladder catheterization, and 2 patients are awaiting their third repair. Conclusion: Unlike the etiology of urogenital fistulas in other countries, most fistulas referred to our unit followed repeat cesarean delivery: none were caused by obstructed labor, and only a few occurred after hysterectomy. Most patients were cured after the primary surgical repair.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".