Selective versus routine use of episiotomy for vaginal births in Shanghai hospitals, China: a comparison of policies
Bibliographic record
Abstract
BACKGROUND: To assess the effects of policy of selective versus routine episiotomy on mother and baby for women delivering vaginally in Shanghai and whether the hospital type has any effect on the outcomes. METHOD: This was a multi-center retrospective cohort study in Shanghai between March 2015 and May 2017. The study population were vaginal births with selective or routine episiotomy (n = 5478) in 20 secondary or tertiary hospitals. Main Outcome Measure was the incidence of severe perineal lacerations. The adjusted odds ratios (aOR) and 95% confidence intervals (CI) were estimated by logistic regression and presented as the effect sizes. All models were stratified by the utilization of level (secondary and tertiary) and type (general and Obstetric) of hospital. RESULTS: The primary outcome was not significantly different between vaginal births with routine and selective episiotomy. Patients with selective episiotomy had a lower risk of postpartum hemorrhage, and newborns in the selective episiotomy group had a lower risk of shoulder dystocia and Neonatal Ward compared to those with routine episiotomy. Newborns in selective episiotomy group had a lower risk of birth injury in tertiary hospital. However, newborns in selective episiotomy group had a higher risk of birth injury in general hospitals. CONCLUSION: Selective episiotomy is safe and can be recommended over routine episiotomy in obstetric and tertiary hospital settings in China.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".