Effectiveness of Emergency Department Asthma Management Strategies on Return Visits in Children: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Emergency departments play an important role in the care of children with asthma. Emergency department return-visit rates provide a measure of the quality of acute asthma care. OBJECTIVE: Our goal was to describe the characteristics of children treated in emergency departments for asthma, the resources and asthma management strategies used by emergency departments, and their effect on return visits within 72 hours. DESIGN, SETTING, AND PATIENTS: We used a population-based cohort study that incorporated both comprehensive administrative heath and survey data from all 152 emergency departments in Ontario, Canada. We studied all 2- to 17-year-old children who had a visit to an emergency department for asthma from April 2003 to March 2005. RESULTS: A total of 32,996 children (>9% of children with asthma in Ontario) had at least 1 visit to an emergency department for the care of asthma, and most of these visits (68.5%) were triaged as high acuity. The vast majority (148 of 152 [97%]) of emergency departments reported using at least 1 asthma management strategy, and 74% used 3 or more. The overall return-visit rate was 5.6%. Logistic regression models that accounted for the clustering of patients in emergency departments and controlled for patient and emergency department characteristics indicated that preprinted order sheets and access to a pediatrician for consultation were strategies significantly associated with a reduction in return visits. The 11 (17%) emergency departments that used both of these strategies had return visit rates of 4.4% compared with 6.9% in the 95 (63%) that used neither strategy. CONCLUSIONS: Emergency departments use a range of strategies to manage asthma in children. Preprinted order sheets and access to pediatricians are associated with important reductions in return-visit rates, and more emergency departments should consider using these strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".