Opioid utility for dyspnea in chronic obstructive pulmonary disease: a complicated and controversial story
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is common chronic respiratory disorder, predominantly caused by exposure to cigarette smoke or biomass fuels, and it usually affects older adults. Dyspnea in COPD that is unresponsive to traditional management is a challenging disease complication for both the patient and the health care professional. Off-label use of opioids has been advocated as a pharmacotherapy strategy for refractory dyspnea. However, negative respiratory outcomes are a potential concern with opioids drugs, especially among individuals with COPD. In this review, randomized controlled trials evaluating opioid efficacy among individuals with COPD are reviewed and critically analyzed, and data from observational drug safety studies is also presented. In summary, the evidence in support of using opioids for refractory dyspnea in COPD is minimal and weak, and there is mounting data demonstrating that opioids are associated with increased respiratory-related morbidity and mortality in this population. Therefore, current evidence does not support the broad application of opioids for refractory dyspnea among individuals with COPD. However, there may be subsets of individuals that experience modest improvement in dyspnea with opioids, and better understanding predictors and mechanisms of such opioid responsiveness should be a focus of future research endeavours.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".