New evidence-based tool to guide the creation of asthma action plans for adults.
Bibliographic record
Abstract
OBJECTIVE: To improve the use of asthma action plans (AAPs) among primary care physicians. SOURCES OF INFORMATION: In a 2017 article, recent asthma guidelines and adult studies (January 2010 to March 2016) addressing acute loss of asthma control were reviewed to develop an evidence-based tool to help guide physicians in creating AAPs to maximize adherence and minimize errors. Evidence supporting the effects of AAPs is level I. Evidence supporting the recommendations in the tool ranges from level I to consensus. MAIN MESSAGE: A lack of knowledge about and training in creating appropriate AAP content is an important barrier to the use of AAPs, as is the fact that instructions provided by asthma guidelines are often difficult to integrate into real-world practice. In order to address these issues, a freely accessible, practical, evidence-based tool has recently been created, addressing both the knowledge and the practical barriers to AAP creation. This tool has been formatted as a printable bedside chart for the point of care, but could also be integrated into a computerized electronic decision support system in the future. CONCLUSION: Asthma action plans, in conjunction with asthma education and regular follow-up, can improve patients' symptoms and quality of life and reduce hospitalization. This novel point-of-care tool provides practical advice on how to complete AAPs to improve patients' asthma self-management.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".