Asthma education and specialized care after pediatric emergency department visits: Real-life impact
Bibliographic record
Abstract
BACKGROUND: Although asthma education and medical follow-up improve asthma control in efficacy trials, implementation issues may affect real-life effectiveness.OBJECTIVES: To assess the real-life impact of asthma education and/or specialized asthma care (SAC), in children referred to the Asthma Education Center (AEC) following an emergency department (ED) visit for asthma.METHODS: We conducted a retrospective cohort study of children aged 0-17 years, presenting to the ED between 2001 and 2006 for asthma, and referred to the AEC. Patients were considered exposed to AEC or SAC from their first visit, exposed to both AEC and SAC when both services had been received and unexposed otherwise. A Cox proportional hazards model was used to estimate the association of AEC and/or SAC with time-to-subsequent asthma ED visit in the 12 months following the index visit.RESULTS: Of the 1,233 children (mean age: 4.4 years; 63.5% male), 46% received both AEC and SAC; 19%, AEC alone, and 8%, SAC alone; 56% AEC with SAC occurring <1 month of the index ED visit. Compared to unexposed, the likelihood (HR [95% CI]) of a subsequent ED visit was significantly lower in children receiving AEC with SAC (0.43 [0.34, 0.53]) and AEC alone (HR = 0.68 [0.53, 0.86]), but not in those receiving SAC alone (HR = 0.85 [0.64, 1.14]).CONCLUSION: Following an AEC referral after an ED visit, children who received AEC alone or with SAC, had a lower likelihood of a subsequent ED visit; no significant reduction was noted with SAC alone, underlying the real-life effectiveness and importance of asthma education.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".