Trend of Cesarean Section Rates and Related Factors Among First-Time Mothers with Single Pregnancies in Zhejiang Province, China: Evidences from a Multi-Center Study
Bibliographic record
Abstract
Bingqing Liu,* Mustafe Abdalle Abdi,* Yuanying Ma Women’s Hospital School of Medicine Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yuanying Ma, Women’s Hospital, Zhejiang University School of Medicine, Xueshi Road 1, Hangzhou, Zhejiang Province, 310003, People’s Republic of China, Email mayuanying88@zju.edu.cnPurpose: The overuse of cesarean section (C-section) is a worldwide public health concern, the most effective measure lies in reducing the rate among primiparous women. We aimed to describe the trend, propose reference values and analyze risk factors of C-section among primiparous women in Zhejiang Province, China.Patients and Methods: We used data of China’s National Maternal Near-Miss Surveillance System from 2012 to 2021. The C-Model was used to calculate the reference values, and logistic regression analysis was employed to explore risk factors.Results: The C-section rate for primiparous women initially decreased and then rose again, the average rate was 36.1%, with a reference C-section rate of 11.8%. In addition to recognized indications for C-section, we also identified advanced maternal age (OR: 3.21, 95% CI: 3.08, 3.35), higher hospital level (OR: 1.15, 95% CI: 1.13, 1.17), higher education level [college or above: 1.05 (1.02, 1.07); high school: 1.1 (1.08, 1.13)], history of abortion (OR: 1.30, 95% CI: 1.28, 1.32), and male infant (OR: 1.15, 95% CI: 1.13, 1.16) as independent risk factors.Conclusion: Reducing the C-section rate for primiparous women by two-thirds was possible in Zhejiang. Systemic health policies were urgently needed to further reduce the C-section rate.Keywords: maternal, C-section, reference C-section rate, C-model, risk factors
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".