Does nebulized fentanyl relieve dyspnea during exercise in healthy man?
Bibliographic record
Abstract
Few therapies exist for the relief of dyspnea in restrictive lung disorders. Accumulating evidence suggests that nebulized opioids selective for the mu-receptor subtype may relieve dyspnea by modulating intrapulmonary opioid receptor activity. Our respective primary and secondary objectives were to test the hypothesis that nebulized fentanyl (a mu-opioid receptor agonist) relieves dyspnea during exercise in the presence of abnormal restrictive ventilatory constraints and to identify the physiological mechanisms of this improvement. In a randomized, double-blind, placebo-controlled crossover study, we examined the effect of 250 μg nebulized fentanyl, chest wall strapping (CWS), and their interaction on detailed physiological and perceptual responses to constant work rate cycle exercise (85% of maximum incremental work rate) in 14 healthy, fit young men. By design, CWS decreased vital capacity by ∼20% and mimicked the negative consequences of a mild restrictive lung disorder on exercise endurance time and on dyspnea, breathing pattern, dynamic operating lung volumes, and diaphragmatic electromyographic and respiratory muscle function during exercise. Compared with placebo under both unrestricted control and CWS conditions, nebulized fentanyl had no effect on exercise endurance time, integrated physiological response to exercise, sensory intensity, unpleasantness ratings of exertional dyspnea. Our results do not support a role for intrapulmonary opioids in the neuromodulation of exertional dyspnea in health nor do they provide a physiological rationale for the use of nebulized fentanyl in the management of dyspnea due to mild restrictive lung disorders, specifically those arising from abnormalities of the chest wall and not affiliated with airway inflammation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".