Recent discoveries from clinical trials: why opioids should not be used for dyspnea management in COPD
Bibliographic record
Abstract
INTRODUCTION: Chronic breathlessness among persons with chronic obstructive pulmonary disease (COPD) is a distressing and limiting symptom and a substantial management challenge for healthcare practitioners. Historically, multiple professional respiratory societies have encouraged the prescription of opioid drugs as a therapeutic intervention for chronic breathlessness. However, in 2024, the European Respiratory Society (ERS) published clinical practice guidelines that markedly departed from such traditional recommendations and stated that opioids should not be used for chronic breathlessness. AREAS COVERED: This manuscript will review recently published, well-designed, randomized controlled trials (literature was searched on PubMed from January 2020 to January 2025) that evaluated the efficacy of oral opioids for chronic breathlessness in persons with COPD and which influenced the new position adopted by ERS in 2024. EXPERT OPINION: Recent, well-designed, adequately powered clinical trials consistently demonstrate that oral opioids are not effective at reducing chronic breathlessness (nor at improving overall quality of life, functional status or exercise tolerance) amongst individuals with advanced COPD. Other professional respiratory societies need to consider and potentially embrace the new ERS position on opioids for dyspnea in COPD, so as to guide members away from an unhelpful, and in some cases harmful, management paradigm.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".