An evidence-based, point-of-care tool to guide completion of asthma action plans in practice
Bibliographic record
Abstract
Asthma action plans (AAPs) reduce healthcare utilisation, improve quality of life and are recommended across guidelines. However, fewer than 25% of patients receive an AAP, partly due to prescribers' inability to complete "yellow zone" instructions (how to intensify therapy for acute loss of control). We sought to review best evidence to develop a practical, evidence-based tool to facilitate yellow zone guidance in adults.We reviewed recent asthma guidelines and adult studies addressing acute loss of asthma control (January 2010 to March 2016). We developed evidence-based rules for yellow zone therapy and operational guidelines to maximise adherence and minimise errors.We reviewed three guidelines and 11 manuscripts (2486 abstracts screened). Recommendations were comparable but some areas lacked guidance. For 15/43 asthma regimens, the commonly recommended four- to five-fold yellow zone inhaled corticosteroid dose increase was problematic due to regulatory dose limits. We identified evidence-based alternatives for 8/15 regimens. Operational guidance included increasing to a maximum of four inhalations while maintaining baseline inhaler frequency and device in the yellow zone.We developed a practical implementation tool to facilitate AAP delivery at the point of care, addressing existing gaps and uncertainties. Our tool should be implemented as part of a multifaceted approach to augment AAP usage.
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 | 0.065 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".