Prevalence of Asthma Remission in CSAR Cohort
Bibliographic record
Abstract
Background: The prevalence of clinical remission in severe asthma is poorly studied. This study aims to assess the prevalence and characteristics of asthma remission based on the Canadian Severe Asthma Registry (CSAR) dataset. Methods: A retrospective review was performed using CSAR cohort data from November 2018 to June 2023. Baseline demographics, asthma medications, and comorbidities were collected. Eligible patients were ≥18 years old, had uncontrolled asthma Global Initiative for Asthma (GINA) step 4 or 5, who were followed for at least 12 months. Three remission criteria were assessed, including rate of asthma exacerbations, use of systemic oral corticosteroid (OCS) and asthma control status. Results: 182 patients met at least one remission criteria, only 58 patients had data for the 3 remission criteria at baselineand 51 patients post-biologics (Bx). Patients started Bx therapy as follows: 36 (20%) on Anti-IgE, 133 (73%) on Anti-IL5/5R, and 13 (7%) on Anti-IL4/13. Compared to pre-Bx period, those who initiated Bx had fewer exacerbations (42% vs 12%), significant reduction in long-term OCS (24% vs 15%) and improvement in asthma control (55% vs 60%). Overall, 27 out of 58 patients met 3 criteria for remission, of which 4 without Bx (6.9%) and 23 with Bx (45%). Increase in remission rates were seen for both Anti-IgE and Anti-IL5/5R. No difference with Anti-IL4/IL13 due to limited sample size. Conclusion: Our CSAR retrospective study demonstrates that biologics therapy enhanced clinical remission up to 45% and led to better outcomes. Nevertheless, further studies are required to investigate the impact of different biological agents and patient characteristics on remission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".