Asthma patients' and physicians’ perspectives on the burden and management of asthma: Post-hoc analysis of APPaRENT 1 and 2 to assess predictors of treatment adherence
Bibliographic record
Abstract
INTRODUCTION: Patient adherence to maintenance medication is critical for improving clinical outcomes in asthma and is a recommended guiding factor for treatment strategy. Previously, the APPaRENT studies assessed patient and physician perspectives on asthma care; here, a post-hoc analysis aimed to identify patient factors associated with good adherence and treatment prescription patterns. METHODS: APPaRENT 1 and 2 were cross-sectional online surveys of 2866 adults with asthma and 1883 physicians across Argentina, Australia, Brazil, Canada, China, France, Italy, Mexico, and the Philippines in 2020-2021. Combined data assessed adherence to maintenance medication, treatment goals, use of asthma action plans, and physician treatment patterns and preferences. Multivariable logistic regression models assessed associations between patient characteristics and both treatment prescription (by physicians) and patient treatment adherence. RESULTS: -agonist (SABA) prescriptions alongside maintenance and reliever therapy (MART). Older age and greater patient-reported severity and reliever use were associated with better adherence. Patient-reported prescription of SABA with MART was associated with household smoking, severe or poorly controlled asthma, and living in China or the Philippines. CONCLUSIONS: Results revealed an important disconnect between patient and physician treatment goals and treatment adherence, suggesting that strategies for improving patient adherence to maintenance medication are needed, focusing on younger patients with milder disease. High reliever use despite good adherence may indicate poor disease control. Personalised care considering patient characteristics alongside physician training in motivational communication and shared decision-making could improve patient management and outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".