Survival in Very Preterm Infants: An International Comparison of 10 National Neonatal Networks
Bibliographic record
Abstract
OBJECTIVES: To compare survival rates and age at death among very preterm infants in 10 national and regional neonatal networks. METHODS: A cohort study of very preterm infants, born between 24 and 29 weeks' gestation and weighing <1500 g, admitted to participating neonatal units between 2007 and 2013 in the International Network for Evaluating Outcomes of Neonates. Survival was compared by using standardized ratios (SRs) comparing survival in each network to the survival estimate of the whole population. RESULTS: Network populations differed with respect to rates of cesarean birth, exposure to antenatal steroids and birth in nontertiary hospitals. Network SRs for survival were highest in Japan (SR: 1.10; 99% confidence interval: 1.08-1.13) and lowest in Spain (SR: 0.88; 99% confidence interval: 0.85-0.90). The overall survival differed from 78% to 93% among networks, the difference being highest at 24 weeks' gestation (range 35%-84%). Survival rates increased and differences between networks diminished with increasing gestational age (GA) (range 92%-98% at 29 weeks' gestation); yet, relative differences in survival followed a similar pattern at all GAs. The median age at death varied from 4 days to 13 days across networks. CONCLUSIONS: The network ranking of survival rates for very preterm infants remained largely unchanged as GA increased; however, survival rates showed marked variations at lower GAs. The median age at death also varied among networks. These findings warrant further assessment of the representativeness of the study populations, organization of perinatal services, national guidelines, philosophy of care at extreme GAs, and resources used for decision-making.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".