Patients' and physicians’ perspectives on the burden and management of asthma: Results from the APPaRENT 2 study
Bibliographic record
Abstract
BACKGROUND: The 2021 Global Initiative for Asthma (GINA) report recommends 2 treatment tracks depending on choice of reliever therapy: either inhaled corticosteroid (ICS)/formoterol, or short-acting β2-agonist (SABA) with ICS to be used whenever a SABA is taken. OBJECTIVE: The Asthma Patients' and Physicians' Perspectives on the Burden and Management of Asthma (APPaRENT) 2 study aimed to understand current real-world treatment approaches and their alignment with GINA recommendations. METHODS: Patients and physicians were recruited for the online survey from online panels from August-November 2021. INCLUSION CRITERIA: adults, physician diagnosis of asthma, ≥6 months prescribed inhaler use (patients); primary care, ≥4 patients with asthma per month, ≥3 years clinical practice (physicians). RESULTS: 1650 patients and 1080 physicians were included. For patients with moderate to severe asthma, physicians prescribed proactive regular dosing (PRD) with ICS/long-acting β2-agonist (LABA) combination with (47%) or without (15%) SABA as initial therapy. Most pulmonologists (75%) and general practitioners (57%) selected a PRD approach. The majority of patients, 85% (79-91%), considered to be using maintenance and reliever therapy (MART), were also prescribed non-ICS rescue inhaler. CONCLUSIONS: Physicians preferred a preventive regular dosing approach to achieve symptom control for patients with moderate to severe asthma, which is more aligned with GINA 2021 Track 2 recommendations than Track 1. Many patients on MART request additional rescue inhalers, suggesting that MART is being misapplied in most instances and that patients may perceive their asthma as inadequately controlled with MART therapy.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".