A multifaceted intervention to improve asthma control via asthma education, medical follow-up and regular self-monitoring after pediatric emergency department visits for asthma
Bibliographic record
Abstract
BACKGROUND Repeated emergency department (ED) visits for acute asthma signal poor asthma control.OBJECTIVE We examined the effectiveness of a multifaceted intervention initiated in the ED to promote asthma education, medical visits and regular self-monitoring and to reduce morbidity following an acute care visit for asthma in children.METHODS We conducted a single-blind randomized controlled trial in children aged 5–17 years presenting for a second or more ED visits for asthma in the past year. The main outcome was the risk of asthma acute care visits.RESULTS We randomized 298 children to usual care (N = 127) and the intervention (N = 171); Children were mostly Caucasians with a median (25%, 75%) age of 8 (6, 11) years. Compared to usual care, significantly more intervention patients attended a medical follow-up visit (32% vs 20%, RR: 1.54 [95%CI: 1.03, 2.32]) and an asthma education session (28% vs 9%, RR: 3.24 [1.75, 5.99]) within 4 weeks of the ED visit. There was no significant group difference in the risk of acute care visits over the next year (RR: 0.92 [0.71, 1.19]).CONCLUSIONS In asthmatic children with recurrent ED visits, promotion of asthma education, medical follow-up visits, asthma control awareness and self-monitoring increased attendance to asthma education and medical follow-up within 4 weeks of the index visit, without reducing exacerbations requiring health care resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".