66 Long-term survival and neurodevelopmental outcomes of very-preterm infants born in Canada between 2009 and 2016
Bibliographic record
Abstract
Abstract Primary Subject area Neonatal-Perinatal Medicine Background Quality improvement programs across Canadian Neonatal Network (CNN) sites have led to increased neonatal survival without major neonatal morbidity among infants born extremely preterm. The next step is to determine if such activities impact longer-term survival and neurodevelopmental outcomes. Objectives This cohort study aimed to compare death or significant neurodevelopmental impairment (sNDI) (Bayley-III scores < 70, severe cerebral palsy, blind, or hearing aided) at 18-24 months corrected age among infants born < 29 weeks’ gestation admitted to CNN sites, between 2 Epochs: 1 (2009-2012) and 2 (2013-2016). Secondary objectives included death or neurodevelopmental impairment (NDI) (Bayley-III < 85, any cerebral palsy, visual or hearing impairment), death, sNDI, NDI, and components of neurodevelopmental impairment. Design/Methods Only sites with ≥ 70% follow-up rates were included. Differences in maternal-infant characteristics and neonatal morbidities were assessed by Pearson Chi-square and Student t-test testing. Adjusted odds ratios with 95% CIs were calculated for outcome change between the 2 Epochs, accounting for patient characteristic differences in the model. Results Study population included 4426 children; Epoch 1: 1895 (43%) and Epoch 2: 2531 (57%). In Epoch 2, more mothers received MgSO4 (56.3% vs. 28.4%; p<0.01), antibiotics (69%vs.65.3%; p 0.01) and delayed cord clamping (37.1% vs. 31.3%; p 0.02), and fewer infants had SNAP-2 (illness severity score) >20 (30.7% vs. 35.2%; p<0.01) or late-onset sepsis (23.3% vs. 26.9%; p 0.01). See Table 1. Conclusion Significant reductions in rate of death or sNDI, and in visual and hearing impairment, were identified between Epoch 2 to Epoch 1. An increase in poor cognitive outcome rates requires further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".