The dyspnea target: can we zero in on opioid responsiveness in advanced chronic obstructive pulmonary disease?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite recognition that opioids alleviate dyspnea in chronic obstructive pulmonary disease, many clinicians remain reluctant to use them outside of the final days of life. This article defines the term 'opioid responsiveness' as it pertains to dyspnea, and poses that it may be possible to predict more accurately the patients with chronic obstructive pulmonary disease and refractory dyspnea who might benefit from opioid treatment for either short-term use during episodes of dyspnea crisis or for long-term use for chronic dyspnea on minimal exertion. RECENT FINDINGS: This article highlights recent evidence of known dimensions contributing to the sensation of dyspnea in chronic obstructive pulmonary disease and builds on clinical observational experience to generate a conceptual model of opioid responsiveness. The 'dyspnea target' and 'opioid responsiveness score' are proposed as a means of defining more clearly the sensations encountered by a given patient and the likelihood of symptomatic improvement in response to opioids. SUMMARY: The 'dyspnea target' or similar conceptual models that attempt to predict how specific aspects of dyspnea may affect response to treatments offer clinicians the potential to more effectively target interventions. The model is presented in its theoretical stage in order to stimulate further discussion and research in an area of current interest.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".