Asthma Worsenings: Approaches to Prevention and Management from the Asthma Worsenings Working Group
Bibliographic record
Abstract
Most asthma patients prescribed maintenance asthma therapies still experience periods of asthma worsenings characterized by daytime or night-time symptoms, or an increased need for rescue medication. In fact, these episodes are highly prevalent even in patients with well-controlled disease. Published literature suggests that asthma worsenings likely represent a window of opportunity during which patients could intervene early to prevent exacerbations or further deterioration of asthma symptoms. However, current evidence suggests that most patients fail to respond or to self-manage appropriately during these periods.To address the issue of asthma worsenings, an interdisciplinary committee of respirologists, allergists, family physicians, pharmacists and certified asthma educators from across Canada developed a practical definition of asthma worsenings and provided approaches to the prevention and management of these episodes based on current literature. To date, combination inhaled corticosteroid/long-acting beta-agonist therapy, particularly single inhaler maintenance and reliever therapy, appears to be an effective strategy for preventing asthma worsenings and exacerbations. Addressing the potential barriers to appropriate patient self-management of asthma worsenings, such as failure to adequately identify and respond to worsenings, low expectations for controlling asthma, low health literacy and poor patient-health care professional communication, are also critical to the successful prevention and management of these episodes. Finally, an interdisciplinary team approach involving patients and their families, certified asthma educators, primary care physicians, pharmacists and specialists is likely to have the greatest impact on the identification, prevention and management of asthma worsenings.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".