Inhaled short-acting bronchodilators for managing emergency childhood asthma: an overview of reviews
Bibliographic record
Abstract
International guidelines provide conflicting recommendations on how to use bronchodilators to manage childhood acute wheezing conditions in the emergency department (ED), and there is variation within and among countries in how these conditions are managed. This may be reflective of uncertainty about the evidence. This overview of systematic reviews (SRs) aimed to synthesize, appraise, and present all SR evidence on the efficacy and safety of inhaled short-acting bronchodilators to treat asthma and wheeze exacerbations in children 0-18 years presenting to the ED. Searching, review selection, data extraction and analysis, and quality assessments were conducted using methods recommended by The Cochrane Collaboration. Thirteen SRs containing 56 relevant trials and 5526 patients were included. Results demonstrate the efficacy of short-acting beta-agonist (SABA) delivered by metered-dose inhaler as first-line therapy for younger and older children (hospital admission decreased by 44% in younger children, and ED length of stay decreased by 33 min in older children). Short-acting anticholinergic (SAAC) should be added to SABA for older children in severe cases (hospital admission decreased by 27% and 74% when compared to SABA and SAAC alone, respectively). Continuous nebulization, addition of magnesium sulfate to SABA, and levosalbutamol compared to salbutamol cannot be recommended in routine practice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".