Predictors of Hospitalization for Children With Croup, a Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVES: We sought to determine predictors of hospitalization for children presenting with croup to emergency departments (EDs), as well as predictors of repeat ED presentation and of hospital readmissions within 18 months of index admission. We also aimed to develop a practical tool to predict hospitalization risk upon ED presentation. METHODS: Multiple deterministically linked health administrative data sets from Ontario, Canada, were used to conduct this population-based cohort study between April 1, 2006 and March 31, 2017. Children born between April 1, 2006, and March 31, 2011, were eligible if they had 1 ED visit with a croup diagnosis. Multivariable logistic regression was used to determine factors associated with hospitalization, subsequent ED visits, and subsequent croup hospitalizations. A multivariable prediction tool and associated scoring system were created to predict hospitalization risk within 7 days of ED presentation. RESULTS: Overall, 1811 (3.3%) of the 54 981 eligible children who presented to an Ontario ED were hospitalized. Significant hospitalization predictors included age, sex, Canadian Triage and Acuity Scale score, gestational age at birth, and newborn distress. Younger patients and boys were more likely to revisit the ED for croup. Our multivariable prediction tool could forecast hospitalization up to a 32% probability for a given patient. CONCLUSIONS: This study is the first population-based study in which predictors of hospitalization for croup based on demographic and historical factors are identified. Our prediction tool emphasized the importance of symptom severity on ED presentation but will require refinement before clinical implementation.
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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 | 0.001 | 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) | 0.001 | 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".