Emerging therapies to prevent acute exacerbations of chronic obstructive pulmonary disease (COPD): A Canadian perspective
Bibliographic record
Abstract
Despite the use of triple therapy (inhaled corticosteroids, long-acting β2-agonists and long-acting muscarinic antagonists), many patients with chronic obstructive pulmonary disease (COPD) continue to experience acute exacerbations of COPD (AECOPD). Given the significant impact of AECOPD on both patients and healthcare systems, reduction of AECOPD is an important goal of COPD management. Numerous promising molecules, targeting a variety of inflammatory pathways, are being studied for treatment of COPD. Biologic molecules developed thus far inhibit either the interleukin 4 receptor (IL-4; dupilumab), IL-5 (mepolizumab), IL-5 receptor (benralizumab), or components of the IL-33/thymic stromal lymphopoietin (TSLP) alarmin inflammatory pathway (astegolimab, itepekimab, tezepelumab and tozorakimab). Of new small molecules, the most promising include a phosphodiesterase PDE3 and PDE4 inhibitor (ensifentrine), and a PDE4 inhibitor (tanimilast). Phase III trial data for dupilumab, mepolizumab and ensifentrine demonstrate their potential to reduce AECOPD frequency and improve outcomes in high-risk individuals. We discuss the clinical implications of targeted versus broad therapies and the potential for new, emerging therapies to address the unmet needs of high-risk patients with COPD. As more efficacy data emerge, these novel therapies will hopefully be incorporated into evidence-based treatment guidelines to provide improved care and health-related quality of life for patients with COPD.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".