Asthma care pathways in the emergency department
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to describe recent evidence of the efficacy and effectiveness of clinical pathways for the assessment and management of severe acute asthma in children and adults in the emergency department (ED). The review will highlight examples of successful knowledge translation initiatives and their ability to support adherence to Best Practice Guidelines. RECENT FINDINGS: Recent studies reveal that management of pediatric and adult asthma in the ED setting often differs from that which is recommended in clinical practice guidelines. Single and multicenter North American studies have consistently found care gaps. Barriers to adherence to evidence-based management guidelines are numerous. Care pathways are knowledge translation tools that provide a means of applying knowledge translation principles to overcome these barriers, integrate guidelines into practice and optimize patient outcomes. Evidence from a recent Ontario multicenter asthma clinical pathway initiative is highlighted, demonstrating increased adherence to certain aspects of ED care, improved patient recollection of teaching done in the ED and increased referral rates. These findings strengthen the evidence supporting the development and implementation of standardized evidence-based asthma clinical pathways. SUMMARY: Gaps between current and best practices persist for the management of asthma in children and adults in North American EDs. There is robust evidence in support of ED asthma clinical pathways to optimize asthma care and outcomes in this setting.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".