Geographical Attributes, Distribution, and Determinants of Pelvic Organ Prolapse in Midwestern Nepal
Bibliographic record
Abstract
The study investigated the geographical attributes, distribution, and determinants of pelvic organ prolapse (POP) in Nepal to comprehend the underlying factors contributing to its high prevalence in the region. Conducted in the Panchapuri municipality of midwestern Nepal, this cross-sectional study surveyed 103 married women aged 20–49 years using a semi-structured questionnaire. Participants were randomly selected from four health facility catchment areas in equal proportions. Descriptive statistics presented the data while binary logistic regression models assessed factors associated with POP. Participant allocations were mapped using ArcGIS, with shapefiles obtained from official sources. The study revealed a POP prevalence of 37.9%, with housewives exhibiting a significant association compared to businesswomen (AOR: 5.291; 95% CI: 1.046, 26.775). Constipation during pregnancy was significantly associated with POP (AOR: 9.104; 95% CI: 2.210, 37.501), while multipara women with a parity of four or more were 7.8 times more likely to have POP. Interestingly, geographical attributes like altitude, slope, and climate showed no association with POP. The findings underscore the significant association of POP with factors such as housewives, pregnancy-related constipation, and multiparity. Addressing these determinants through targeted research is vital in alleviating the burden of POP. This study emphasizes the urgent need for interventions, policies, and healthcare support, particularly focusing on maternal health and occupational well-being among rural housewives in Nepal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".