The risk of urinary incontinence among women with obese in China： a meta-analysis
Bibliographic record
Abstract
Abstract Objective The objective of this meta-analysis is to assess the potential association between obesity and overweight, and the susceptibility to Urinary Incontinence (UI) among Chinese women. Methods We searched PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI) and WANFANG Database to obtain observational study published between the establishment of the database and 10 July 2023. We used the Newcastle Ottawa Scale (NOS) and the Quality Assessment Program of the American Institute for Healthcare Quality and Research (AHRQ) to evaluate the quality of the study. When P > 0.1 and I2 ≤ 50%, a fixed effect model is used. Otherwise, a random effects model is applied. Funnel plots and Egger's test were used to explore publication bias. All statistical analyses were conducted in Stata 14.0. Results This meta-analysis comprises 13 observational studies involving a total of 76,606 individuals. The pooled analysis reveals no statistically significant association between overweight and the risk of UI (odds ratio [OR] = 1.23; 95% confidence interval [CI]: 0.97–1.56; I2 = 94.6%, P = 0.000). Among Chinese women, obesity significantly increases the likelihood of developing urinary incontinence (OR = 2.00; 95% CI: 1.55–2.58; I2 = 88.8%, p = 0.000). Subgroup analysis demonstrates no significant association between obesity and mixed urinary incontinence (MUI) among obese women in China (OR = 1.31; 95% CI: 0.98–1.75; I2 = 0.0%, P = 0.806). Nevertheless, obesity is significantly associated with stress urinary incontinence (SUI) (OR = 1.72; 95% CI: 1.47–2.01; I2 = 34.3%, P = 0.201). Subgroup analysis of regional types shows that obesity in southern and northern China, eastern, central and western regions is associated with a high risk of UI. Conclusions Obesity is found to be positively associated with an elevated likelihood of UI in Chinese women. This study can provide basis for the prevention and treatment of UI, and provide better prevention and management for alleviating the symptoms of UI in Chinese adult women.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".