Identification and definition of asthma–COPD overlap: The CanCOLD study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Lack of consensus on diagnosis of ACO limits our understanding of the impact, management and outcomes of ACO. The present observational study aims to describe the prevalence, clinical characteristics and course of individuals with ACO based on various definitions used in clinical practice. METHODS: We included individuals with COPD from the prospective, multisite CanCOLD study and defined subjects with ACO using seven definitions commonly used in the literature. RESULTS: Data including questionnaires, lung function and CT scans were analysed from 522 individuals with COPD who were randomly recruited from the population. Among them, 264 fulfilled at least one of the seven definitions of ACO. Prevalence of ACO varied from 3.8% to 31%. Regardless of the definition, individuals with ACO had worse outcomes (lung function and higher percentage of fast decliners, symptoms and exacerbations, health-related quality of life and comorbidities) than the remaining patients with COPD. Conversely, patients with non-ACO had higher emphysema and bronchiolitis scores. The three definitions that included atopy and/or physician diagnosis of asthma identified subjects who differed significantly from patients with COPD. The two ACO definitions with post-bronchodilator reversibility were concordant with COPD and were the least stable, with less than 50% of the patients from each group maintaining reversibility over visits. CONCLUSION: Atopy and physician-diagnosed asthma are more distinguishing characteristics to identify ACO. This finding needs to be validated using measures of airway inflammation and other specific biomarkers.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".