Risk factors for perimenopausal depression in Chinese women: a meta-analysis
Bibliographic record
Abstract
Objective To systematically evaluate the risk factors for perimenopausal depression in Chinese women and to provide a basis for screening perimenopausal women at high-risk for depression. Methods A computer search of seven databases, including SinoMed, PubMed, Web of Science, and so on, and two clinical trial registries on the risk factors for depression in Chinese women during perimenopause was conducted for meta-analysis. The search time limit was from the establishment of the database to December 2022. The included case–control and cross-sectional studies were evaluated using the Newcastle–Ottawa scale (NOS) and criteria developed by the Agency for Healthcare Research and Quality (AHRQ). Results A total of 15 papers with 12,168 patients and 18 risk factors were included. Meta-analysis results showed that the risk factors for depression in perimenopausal women were relationship quality [OR = 1.23, 95% confidence intervals (1.03, 1.46)], marital status [OR = 2.49, 95% CI (1.77, 3.50)], family income [OR = 1.48 95% CI (1.10, 2.00)], comorbid chronic diseases [OR = 2.39, 95% CI (1.93, 2.95)], exercise status [OR = 1.63, 95% CI (1.26, 2.11)], perimenopausal syndrome [OR = 2.36, 95% CI (2.11, 2.63)], age [OR = 1.04, 95% CI (1.01, 1.07)], and stressful events [OR = 12.14, 95% CI (6.48, 22.72)], and social support was a protective factor [OR = 0.76, 95% CI (0.63, 0.91), p < 0.05]. Conclusion Based on the exploration of risk factors for perimenopausal depression in Chinese women, we aimed to provide guidance for the screening of risk factors for depression in perimenopausal women and thereby reduce the incidence of depression. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/#myprospero , CRD42023403972.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".