94: Variations in Mortality of Very Preterm Neonates Between Eight National Neonatal Databases: The iNeo Experience
Bibliographic record
Abstract
Differences in mortality of very preterm neonates have been reported by national neonatal networks. These may reflect variations in case-mix, practices and population coverage of database. Valid comparisons aimed at identifying reasons for variation between countries require adjustment for potential confounders. To compare mortality rates prior to discharge of very preterm neonates using eight national neonatal databases from nine member countries of the International Network for Evaluating Outcomes in Neonates (iNeo). Data on neonates of 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). Mortality (all causes) was compared between each country and all others using standardized mortality ratios (SMR) and pair-wise comparisons were performed using AOR (95% CI). Subgroup analyses were conducted for neonates 24 to 28 weeks GA. 58004 neonates were included in the analyses. Mortality ranged from 5% to 17% between countries. SMRs are presented in the Figure. Adjusted OR (95% CI) are presented in the Table. Subgroup analyses of infants 24 to 28 weeks GA provided similar results. Variations in mortality between countries remained after adjustment for available confounders. Explanations for this variation may lie in differences in population coverage, recording of delivery room deaths, death after discharge from Level 3 NICUs, services organization, unmeasured characteristics, or care practices.
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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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".