Feasibility of using pessaries for treatment of pelvic organ prolapse in rural Nepal
Bibliographic record
Abstract
OBJECTIVE: To evaluate the acceptability, feasibility, and outcomes of pessary fitting in rural Nepali communities. METHODS: A 1-year prospective cohort study was conducted in the Ramechhap district of Nepal in January 2013-January 2014 among women attending a free gynecology health camp. All women with symptomatic pelvic organ prolapse (POP) were offered ring pessaries. Demographic information was collected and questionnaires on POP were completed. A urogynecologic examination was performed. At the 1-year follow-up, women were questioned on pessary use and underwent an examination. Logistic regression was used to identify associations. RESULTS: In total, 411 women attended the health camps, of whom 142 presented with symptomatic POP. Initial fitting was accomplished for 134 (94.4%) of the women. At the 1-year follow-up, 130 (97.0%) women in the cohort were evaluated, and 72 (55.4%) were still using the pessary. The primary reason for discontinuation was the pessary falling out (35/58, 60.3%). The most common complication was vaginal erosion (18/130, 13.8%), observed exclusively among postmenopausal women. Postmenopausal status was a predictor of continued use (odds ratio 3.12, 95% confidence interval 1.45-6.72; P=0.004). CONCLUSION: Pessaries were found to be an acceptable and feasible option with minimal complications for treating POP in rural Nepal.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".