Mortality and Morbidity in Infants <34 Weeks' Gestation in 25 NICUs in China: A Prospective Cohort Study
Bibliographic record
Abstract
Objectives: To describe the rates and variability of mortality and morbidity of preterm infants born in China. Methods: This prospective cohort study included infants born at <34 weeks’ gestation and admitted to 25 NICUs within 7 days of birth between May 1st, 2015 and April 30th, 2016. Infants were followed until death or NICU discharge. The primary outcome was a composite of mortality or any major morbidity (sepsis, necrotizing enterocolitis, intraventricular /periventricular leukomalacia, retinopathy of prematurity, and bronchopulmonary dysplasia) in infants who received complete care following medical advice. Secondary outcomes included rate of discharge against medical advice, mortality and individual morbidities. Results: Of the 8065 infants, 6852 (85%) received complete care and 1213 (15%) were discharged against medical advice. Among infants who received complete care, the rate of the composite outcome was 27% (1827/6852), mortality 4% (248/6852), sepsis 14% (990/6852), necrotizing enterocolitis 3% (191/6550), intraventricular hemorrhage/ periventricular leukomalacia 7% (422/6307), retinopathy of prematurity 2% (67/3349), and bronchopulmonary dysplasia 9% (616/6852). There were significant variations between NICUs for all outcomes. Conclusions: Discharged against medical advice, mortality, and morbidity rates for preterm infants <34 weeks’ gestation are high in China with significant variations between NICUs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".