96: Comparison of Mortality and Major Morbidity of Very Preterm Neonates Using Data from Eight National Neonatal Databases: The iNeo Experience
Bibliographic record
Abstract
There is variability between countries in the health outcomes of very preterm neonates. Valid outcome comparisons aimed at identifying areas of improvement require adjustment for case-mix. To compare mortality and major morbidity of very pre-term neonates between eight national neonatal databases from nine member countries of the International Network for Evaluating Outcomes in Neonates (iNeo). Data on neonates born at 24 to 31 weeks GA, BW <1500 g, without major congenital anomaly and registered in national databases were retrieved from the iNeo database (2007–10). A composite outcome of mortality or any major morbidity (grade 3/4 IVH, PVL, treated ROP, or CLD) was compared between countries using standardized ratios (SR) and AOR (95% CI). 58004 neonates were included in the analyses. The composite outcome rate varied from 26–42%. SR comparing the composite outcome between each country and all others are presented in the Figure. Marked variation in the composite outcome was identified between countries. Explanations for this variation may lie in different data definitions, data recording, health services organization, unmeasured population characteristics, or care provision practices. These results provide an opportunity for future detailed exploration of areas amendable to improvement.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".