Diagnosis and management of asthma, COPD and asthma‐COPD overlap among primary care physicians and respiratory/allergy specialists: A global survey
Bibliographic record
Abstract
INTRODUCTION: Asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) is a heterogenous condition with clinical features shared by both asthma and COPD. OBJECTIVES: This online global survey of respiratory/allergy specialists and primary care practitioners (PCPs) was performed to understand current clinical approaches to the differential diagnosis and management of asthma, COPD and ACO. METHODS: Respondents were recruited through: (a) a global online physician respondent community (49,980 PCPs and 7205 specialists); (b) market research agents; (c) experts; (d) professional societies; (e) colleague invitation. Respondents were presented with a survey including hypothetical clinical scenarios of diagnostic uncertainty to identify management approaches. RESULTS: 891 responses (447 PCPs and 444 specialists) were collected across 13 countries. Reported features used for diagnosis of asthma and COPD were consistent with practice guidelines, but there was variability in those selected for ACO diagnosis. Features typically selected by specialists focused on spirometry/history, while PCPs focused on previous treatment/symptoms. Most respondents could correctly diagnose patients with features of ACO; however, features selected for theoretical diagnosis were often different to those selected in the case scenarios. Additionally, treatment selection was often inconsistent with guidelines, with over half of respondents not recommending inhaled corticosteroids in a patient with ACO and dominant features of asthma. CONCLUSION: While most PCPs and respiratory/allergy specialists can reach a working diagnosis of ACO, there remains uncertainty around which diagnostic features are most important and what constitutes optimal management. It is imperative that clinical studies including patients with ACO are initiated, allowing the generation of evidence-based management strategies.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".