Perinatal Characteristics and Neonatal Outcomes of Singletons and Twins in Chinese Very Preterm Birth Infants: A Cohort Study
Bibliographic record
Abstract
Abstract Background: The prevalence of preterm birth has been raising, and there is a paucity of nationwide data on the perinatal characteristics and neonatal outcomes of twin deliveries of very preterm infants (VPIs) in China. This study compared the perinatal characteristics and outcomes of singletons and twins admitted to neonatal intensive care units (NICUs) in China. Methods: The study population comprised all infants born before 32 weeks in Chinese Neonatal Network (CHNN) between January 2019 and December 2019. Results: During the study period, there were 6634 (71.2%) singletons and 2680 (28.8%) twins, with mean birth weight of 1333.70g and 1294.63g, respectively. Twins were significantly more likely to be delivered by cesarean section (p<0.01), to have antenatal steroid usage (p=0.048), to have received assisted reproductive technology (ART) (p<0.01), and have higher prevalence of maternal diabetes (p<0.01) and more were inborn (p<0.01) compared with singletons. In addition, twins had a lower prevalence of small for gestational age, maternal hypertension, primigravida compared to singletons (all p<0.01). After adjusting for the potential confounders, twins had higher mortality (adjusted odds ratio [AOR] 1.28, 95% confidence interval [CI] 1.10-1.49), short term composite outcome (AOR 1.28, 95% CI 1.09-1.50), respiratory distress syndrome (RDS) (AOR 1.30, 95% CI 1.12-1.50), bronchopulmonary dysplasia (BPD) (AOR 1.10, 95% CI 1.01-1.21), surfactant usage (AOR 1.22, 95% CI 1.05-1.41) and prolonged hospital stay (adjusted mean ratio 1.03, 95% CI 1.00-1.06), compared to the singletons. Conclusion: Our work suggest that twins have a greater risk of mortality, higher incidence of RDS and BPD, more surfactant usage, and longer NICU stay compared with singletons among VPIs 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".