Risk Factors for Urinary Incontinence in Chinese Women: A Cross-sectional Survey
Bibliographic record
Abstract
OBJECTIVE: Urinary incontinence is highly prevalent among women, with a substantial effect on health-related quality of life. This article aimed to investigate the independent factors for urinary incontinence (UI) and the relative importance of each factor. METHODS: This study was a cross-sectional survey of Chinese women in Guangzhou. Female 20 years and older were invited to participate. The International Consultation on Incontinence Questionnaire-Urinary Incontinence Short Form was used to determine whether respondents are experiencing UI. Univariate and multivariate unconditional logistic regression analyses were performed to determine the significant risk factors associated with UI. RESULTS: A total of 2626 women were invited to participate in the survey. The response rate was 80.5% (2114/2626). The prevalence of UI among the study population was 31.2%. Old age, increased body mass index, childbirth, family history of any female pelvic floor disorders, symptoms of chronic cough or rhinitis, wearing a corset, and often drinking were independent risk factors for UI. CONCLUSIONS: Urinary incontinence is common among Chinese women in Guangzhou. Among the factors that we are concerned with, old age and vaginal delivery are the two with greatest impact. Moreover, wearing a corset and drinking are the 2 lifestyle factors associated with UI.
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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 | 0.001 |
| 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 | 0.000 |
| 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".