97: Determinants of Hospital Re-Admission Following Neonatal Discharge of Extremely Preterm Infants in Canada
Bibliographic record
Abstract
Survivors of preterm birth are at higher risk of re-hospitalisation during infancy, especially for respiratory-related conditions. Determinants of hospital re-admission have not been studied comprehensively in Canadian preterm-born infants. To examine whether health-related, sociodemographic and geographical factors are associated with hospital re-admission among extremely preterm infants in Canada. A total of 818 preterm infants born at <29 weeks gestation between January 1st and December 31st 2010 and followed at 18 to 24 months corrected age (CA) in 26 Canadian Neonatal Follow-Up Network centers were studied. Data was collected through chart review and parental interview using standardised forms. All infants underwent a neurological examination. The association between hospital re-admission and child/family characteristics was assessed by Pearson χ2 analyses for categorical variables and by ANOVA F-test for continuous variables. From neonatal discharge to 18 months CA, 271 infants (33%) were re-admitted 377 times. Re-hospitalization rates ranged from 50% for infants born at ≤23 weeks to 29% for those born at 28 weeks. Most common reasons were respiratory issues (50%), surgery (23%), infections (8%) and growth-related problems (5%). Comparisons between children re-hospitalized vs. not re-hospitalized revealed longer neonatal stay (88 vs. 73 days; P<0.01), greater proportion of infants neurologically abnormal at 18 months (20% vs. 8%; P<0.01), fewer primary caregivers subsequently employed (53% vs. 63%; P=0.02) and more on social welfare (16% vs. 8%; P=0.02) at 18 months, as well as more infants from the First Nation (8% vs. 3%, P=0.04). Marked variations across the country were observed with re-hospitalization rates between 19% and 50% for various provinces. Re-hospitalization of extremely preterm infants is related to neonatal, socio-demographic and regional factors. Whether geographical variations are explained by population characteristics or hospital-related practices remains to be explored.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".