APPaRENT 1 & 2 surveys: post-hoc analysis to assess predictors of asthma treatment adherence
Bibliographic record
Abstract
Introduction: Good adherence to maintenance medication is key to improve asthma outcomes. Aims: Identify patient characteristics associated with adherence and ways to improve current asthma care through survey post-hoc analysis. Methods: APPaRENT 1&2 were online surveys across 9 countries in 2020–2021 assessing patient and physician perspectives on asthma management. Patients were aged ≥18 years with self-reported history of physician-diagnosed asthma. Good adherence was defined as inhaled corticosteroid (ICS)-based treatment ≥once a day. Results: Patients (N=2866) and physicians (N=1883) reported good adherence rates of 60.5% vs 70.8%, respectively, and differing perspectives on treatment and goal setting. Older age, more severe disease, and more frequent reliever use were associated with higher odds of good adherence (Figure). Likelihood of good regimen adherence was higher with ICS proactive regular dosing vs maintenance and reliever therapy (MART). Use of an additional reliever with MART was associated with household smoking, severe or poorly controlled asthma, and living in China or the Philippines. Conclusions: Age, asthma severity and reliever use should be considered when personalising asthma care to optimise adherence and clinical outcomes. More physician-patient collaboration to agree on goals, encourage appropriate medication use and regular follow-ups would advance care. Funding: GSK (Study IDs: 212911&214325). erj;64/suppl_68/PA4491/F1 F1 F1
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".