International comparison of emergency hospital use for infants: data linkage cohort study in Canada and England
Bibliographic record
Abstract
OBJECTIVES: To compare emergency hospital use for infants in Ontario (Canada) and England. METHODS: We conducted a population-based data linkage study in infants born ≥34 weeks' gestation between 2010 and 2013 in Ontario (n=253 930) and England (n=1 361 128). Outcomes within 12 months of postnatal discharge were captured in hospital records. The primary outcome was all-cause unplanned admissions. Secondary outcomes included emergency department (ED) visits, any unplanned hospital contact (either ED or admission) and mortality. Multivariable regression was used to evaluate risk factors for infant admission. RESULTS: The percentage of infants with ≥1 unplanned admission was substantially lower in Ontario (7.9% vs 19.6% in England) while the percentage attending ED but not admitted was higher (39.8% vs 29.9% in England). The percentage of infants with any unplanned hospital contact was similar between countries (42.9% in Ontario, 41.6% in England) as was mortality (0.05% in Ontario, 0.06% in England). Infants attending ED were less likely to be admitted in Ontario (7.3% vs 26.2%), but those who were admitted were more likely to stay for ≥1 night (94.0% vs 55.2%). The strongest risk factors for admission were completed weeks of gestation (adjusted OR for 34-36 weeks vs 39+ weeks: 2.44; 95% CI 2.29 to 2.61 in Ontario and 1.66; 95% CI 1.62 to 1.70 in England) and young maternal age. CONCLUSIONS: Children attending ED in England were much more likely to be admitted than those in Ontario. The tendency towards more frequent, shorter admissions in England could be due to more pressure to admit within waiting time targets, or less availability of paediatric expertise in ED. Further evaluations should consider where best to focus resources, including in-hospital, primary care and paediatric care in the community.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".