Training and capacity building in obstetric fistula repair: A scoping review
Bibliographic record
Abstract
BACKGROUND: An ongoing barrier to sustainable obstetric fistula (OF) care is the lack of trained fistula surgeons. Despite a standardized training curriculum, data regarding OF repair training remain limited. OBJECTIVES: To assess the availability of literature on the case numbers or training duration required for OF repair competency and whether these data are stratified by trainee background or repair complexity. SEARCH STRATEGY: A systematic search of MEDLINE, Embase, and OVID Global Health electronic databases and gray literature. SELECTION CRITERIA: All English sources from all years from low- and middle-income and high-income countries were eligible. Identified titles and abstracts were screened and full-text articles were reviewed. DATA COLLECTION AND ANALYSIS: Data collection and analysis included a descriptive summary organized by training case numbers, training duration, trainee background, and repair complexity. RESULTS: Of the 405 sources retrieved, 24 were included in the study. The only concrete recommendations were in the International Federation of Gynecology and Obstetrics 2022 Fistula Surgery Training Manual, which proposes 50 to 100 repairs (Level 1), 200 to 300 repairs (Level 2), and trainer discretion for Level 3 competency. CONCLUSIONS: More case- or time-based data, particularly if stratified by trainee background and repair complexity, would be useful at the individual, institutional, and policy level for fistula care implementation or expansion.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".