Neonatal and infant readmissions for late preterm and early term babies in Ontario and England: a cohort study using linked population-level healthcare data
Bibliographic record
Abstract
ABSTRACTBackgroundBabies born late preterm (34-36 weeks gestation) or early term (37-38 weeks) are at increased risk of unplanned readmissions compared with full-term babies. We examined differences in neonatal and infant outcomes in England and Ontario. MethodsLinked maternity-baby hospitalisation data were extracted from two universal healthcare systems, Ontario (n=702,565; 2005-2013) and England (n=1,165,375; 2011-2013). We modelled rates of unplanned readmissions within 30-days post-discharge of delivery, and readmissions, emergency department (ED) visits, deaths and total inpatient days within 12-months post-discharge, adjusting for neonatal, maternal and delivery factors. ResultsThe median newborn length of stay was 4 and 5 days in Ontario and England respectively for late preterm babies, and 2 days in both countries for early term babies. Early neonatal readmissions were lower in Ontario: 4.8% of early term and 7.2% of late preterm babies compared with 8.3% and 11.4% respectively in England (p<0.05). Within 12-months post-discharge, 9.6% of early term and 13.5% of late preterm babies were readmitted in Ontario compared with 24.0% and 30.5% in England (p<0.05); total inpatient days per 100 babies were 36.5 for early term and 61.9 for late preterm (Ontario) compared with 62.7 and 107.6 (England). Infant mortality (0.1-0.4%) and ED visits (40-44%) were similar between countries. ConclusionsUnplanned readmissions and total inpatient stay are significantly higher in England than Ontario for early term / late preterm babies, despite similar ED attendances and lengths of newborn stay. Further investigation of differences in healthcare practices between countries should evaluate access to paediatric primary care and thresholds for admission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".