Characteristics and surgical success of patients presenting for repair of obstetric fistula in western Kenya
Bibliographic record
Abstract
OBJECTIVE: To carry out a large-scale retrospective review of patients who had undergone surgical repair of obstetric fistula in Kenya to determine patient characteristics and determinants of successful surgical repair. METHODS: The patient records of 483 surgical repairs of obstetric fistula treated by a single surgeon (H.M.) between January 2005 and July 2010 at 3 medical centers in western Kenya were retrospectively reviewed. Descriptive and bivariate statistical analyses were performed. RESULTS: Young women with some primary or no education and prolonged labor at the time of first delivery were most highly correlated with obstetric fistula formation. Success of fistula closure was 86% for first-time vesicovaginal fistula (VVF) repairs and 67% for first-time VVF combined with rectovaginal fistula (RVF) repairs. Among women who had previously attempted VVF or combined VVF/RVF repairs, 73% and 50% of fistulas, respectively, were repaired successfully. First-time repair was significantly associated with surgical success compared with patients with a history of previous repair attempts (P=0.027). CONCLUSION: Among Kenyan women presenting for fistula repair, fistula most was most highly correlated with a low level of education and prolonged labor. The findings are consistent with results reported from other countries in Sub-Saharan Africa.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".