Pharmacological strategies to reduce exacerbation risk in COPD: a narrative review
Bibliographic record
Abstract
Identifying patients at risk of exacerbations and managing them appropriately to reduce this risk represents an important clinical challenge. Numerous treatments have been assessed for the prevention of exacerbations and their efficacy may differ by patient phenotype. Given their centrality in the treatment of COPD, there is strong rationale for maximizing bronchodilation as an initial strategy to reduce exacerbation risk irrespective of patient phenotype. Therefore, in patients assessed as frequent exacerbators (>1 exacerbation/year) we propose initial bronchodilator treatment with a long-acting muscarinic antagonist (LAMA)/ long-acting β2-agonist (LABA). For those patients who continue to experience >1 exacerbation/year despite maximal bronchodilation, we advocate treating according to patient phenotype. Based on currently available data on adding inhaled corticosteroids (ICS) to a LABA, ICS might be added to a LABA/LAMA combination in exacerbating patients who have an asthma-COPD overlap syndrome or high blood eosinophil counts, while in exacerbators with chronic bronchitis, consideration should be given to treating with a phosphodiesterase (PDE)-4 inhibitor (roflumilast) or high-dose mucolytic agents. For those patients who experience frequent bacterial exacerbations and/or bronchiectasis, addition of mucolytic agents or a macrolide antibiotic (e.g. azithromycin) should be considered. In all patients at risk of exacerbations, pulmonary rehabilitation should be included as part of a comprehensive management plan.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".