Mortality and significant neurosensory impairment in preterm infants: an international comparison
Bibliographic record
Abstract
OBJECTIVE: To compare mortality and rates of significant neurosensory impairment (sNSI) at 18-36 months' corrected age in infants born extremely preterm across three international cohorts. DESIGN: Retrospective analysis of prospectively collected neonatal and follow-up data. SETTING: Three population-based observational cohort studies: the Australian and New Zealand Neonatal Network (ANZNN), the Canadian Neonatal and Follow-up Networks (CNN/CNFUN) and the French cohort Etude (Epidémiologique sur les Petits Ages Gestationnels: EPIPAGE-2). PATIENTS: Extremely preterm neonates of <28 weeks' gestation in year 2011. MAIN OUTCOME MEASURES: Primary outcome was composite of mortality or sNSI defined by cerebral palsy with no independent walking, disabling hearing loss and bilateral blindness. RESULTS: Overall, 3055 infants (ANZNN n=960, CNN/CNFUN n=1019, EPIPAGE-2 n=1076) were included in the study. Primary composite outcome rates were 21.3%, 20.6% and 28.4%; mortality rates were 18.7%, 17.4% and 26.3%; and rates of sNSI among survivors were 4.3%, 5.3% and 3.3% for ANZNN, CNN/CNFUN and EPIPAGE-2, respectively. Adjusted for gestational age and multiple births, EPIPAGE-2 had higher odds of composite outcome compared with ANZNN (OR 1.71, 95% CI 1.38 to 2.13) and CNN/CNFUN (OR 1.72, 95% CI 1.39 to 2.12). EPIPAGE-2 did have a trend of lower odds of sNDI but far short of compensating for the significant increase in mortality odds. These differences may be related to variations in perinatal approach and practices (and not to differences in infants' baseline characteristics). CONCLUSIONS: Composite outcome of mortality or sNSI for extremely preterm infants differed across high-income countries with similar baseline characteristics and access to healthcare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".