Novel insights into the comorbidity burden of severe asthma in Canada: Analysis of the Canadian Severe Asthma Registry
Bibliographic record
Abstract
Background: The prevalence of comorbidities in severe asthma in Canada is understudied. Our study aims to review the prevalence of comorbidities from the Canadian Severe Asthma Registry (CSAR) dataset and compare them to the global International Severe Asthma Registry (ISAR) cohort. Methods: This retrospective review used CSAR cohort data from 06/2003 to 03/2024 and included 515 patients; the ISAR global cohort data was from 02/2000 to 03/2024 and included 16834 patients. Eligible patients were ≥18 years of age and had uncontrolled Global Initiative for Asthma (GINA) step 4 or 5. We assessed the prevalence of four T2-related and fourteen potentially oral corticosteroid (OCS)-related comorbidities. Results: The CSAR cohort had an average age of 56 years; 63% were female, 59% never-smokers, and mean FEV1 was 82% predicted. Long-term oral corticosteroids (LTOCS) users were comparable (20% vs. 22%) in CSAR and ISAR. On T2-related comorbidities, CSAR had a higher prevalence of allergic rhinitis (68% vs. 54%), chronic rhinosinusitis (72% vs. 48%), nasal polyps (41% vs. 22%), and eczema (45% vs. 12%). On potentially OCS-related comorbidities, CSAR had a higher prevalence of obesity (43% vs. 35%), cataracts (15% vs. 3%), and pneumonia (37% vs. 8%). CSAR had a higher proportion of patients with 3+ T2-related comorbidities (46% vs. 15%). Conclusion: Canadian severe asthma patients have a higher burden of T2-related comorbidities and potentially OCS-related comorbidities than ISAR. This study emphasizes the high prevalence of comorbidities among Canadian severe asthma patients and the importance of addressing those comorbidities in clinical practice.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".