Risk factors for re-hospitalization following neonatal discharge of extremely preterm infants in Canada
Bibliographic record
Abstract
OBJECTIVE: Survivors of extremely preterm birth are at risk of re-hospitalization but risk factors in the Canadian population are unknown. Our objective is to identify neonatal, sociodemographic, and geographic characteristics that predict re-hospitalization in Canadian extremely preterm neonates. METHODS: This is a retrospective analysis of a prospective observational cohort study that included preterm infants born 22 to 28 weeks' gestational age from April 1, 2009 to September 30, 2011 and seen at 18 to 24 months corrected gestational age in a Canadian Neonatal Follow-Up Network clinic. Characteristics of infants re-hospitalized versus not re-hospitalized are compared. The potential neonatal, sociodemographic, and geographic factors with significant association in the univariate analysis are included in a multivariate model. RESULTS: From a total of 2,275 preterm infants born at 22 to 28 weeks gestation included, 838 (36.8%) were re-hospitalized at least once. There were significant disparities between Canadian provincial regions, ranging from 25.9% to 49.4%. In the multivariate logistic regression analysis, factors associated with an increased risk for re-hospitalization were region of residence, male sex, bronchopulmonary dysplasia, necrotizing enterocolitis, prolonged neonatal intensive care unit (NICU) stay, ethnicity, Indigenous ethnicity, and sibling(s) in the home. CONCLUSION: Various neonatal, sociodemographic, and geographic factors predict re-hospitalization of extremely preterm infants born in Canada. The risk factors of re-hospitalization provide insights to help health care leaders explore potential preventative approaches to improve child health and reduce health care system costs.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".