Emergency Department Visit Count: A Practical Tool to Predict Asthma Hospitalization in Children
Bibliographic record
Abstract
To evaluate whether the frequency of asthma-related emergency department (ED) visits could predict future asthma hospitalization in the pediatric population.The study included 2669 children between the ages of 2 and 16.99 years with asthma visits to the ED at the Children’s Hospital of Eastern Ontario between September 2012 to August 2015.This was a retrospective cohort study in which health administrative data were used to identify all pediatric asthma visits to the ED from 2012 to 2015. The first asthma ED visit within the 3-year inclusion period was defined as index ED visit. The primary exposure was the number of asthma ED visits in the preceding 12 months. Each patient was managed for 12 months after the index ED visit to identify the number of asthma-related hospitalizations and subsequent ED visits. The severity of asthma symptoms at each visit was assessed by using the Canadian Triage and Acuity Scale.The number of asthma ED visits in the preceding year was associated with an increased risk of hospitalization in the subsequent 12 months in a dose-dependent manner. The risk of hospitalization was 2.9 (95% confidence interval: 1.6 to 5) times higher in children with 1 previous ED visit and 4.4 (95% confidence interval: 1.9 to 10.4) times higher in children with ≥2 previous ED visits, compared with that of children with no ED visits. Asthma severity on the basis of Canadian Triage and Acuity Scale level was also a risk factor for future asthma hospitalization. The number of previous asthma ED visits was associated with an increased risk of repeat asthma ED visits in the following year.The number of asthma ED visits was identified as an independent marker of future asthma-related hospitalizations and repeat ED visits in pediatric patients.A major goal of asthma management is to reduce the risk of exacerbations leading to ED visits and hospitalization. Poor asthma control leads to school absenteeism and increased health care use. Identification of a reliable marker to predict future asthma risk could help health care providers target interventions to children at high risk. In this study, the authors identified ED visit count as a predictor of future asthma-related hospitalization in pediatric patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".