Risk factors for adverse pregnancy outcomes in Chinese women: a meta-analysis
Bibliographic record
Abstract
Objective: This study examined the associated risk factors of adverse pregnancy outcomes among Chinese females and furnished some fundamental principles and recommendations for enhanced prevention of adverse pregnancy and preservation of women's well-being. Methods: A systematic review was conducted by retrieving the MEDLINE (The National Library of Medicine), Embase, PubMed, and Cochrane databases. The relevant risk factors for adverse pregnancy in Chinese women were retrieved from May 2017 to April 2023. Use Review Manager for data analysis. Calculate the merge effect based on data attributes using mean difference (MD) or odds ratio (or) and 95% confidence interval (CI). The meta-analysis was registered at INPLASY (International Platform of Registered Systematic Review and Meta-analysis Protocols, 202340090). Results: A total of 15 articles were included, with a total of 946,818 Chinese pregnant women. Moreover, all the literature was scored by the NOS (Newcastle-Ottawa Scale), and all literatures were ≥7 points, which were evaluated as high quality. There are seven risk factors related to adverse pregnancy in Chinese women: parity, pregnancy frequency, education level, smoking, gestational diabetes, gestational weeks, and age. Moreover, the main risk factors for adverse pregnancy are pregnancy frequency, education level, gestational diabetes mellitus, and age. Conclusion: The pregnancy frequency, education level, gestational diabetes mellitus, and age were significantly associated with the adverse pregnancy in Chinese women, whereas gestational weeks, smoking, and parity had no significant effect on adverse pregnancy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| 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) | 0.000 | 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 teacher head, 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".