Heliox moderates multidimensional domains of exertional dyspnoea in people with COPD
Bibliographic record
Abstract
Introduction Avoidance and premature cessation of exercise resulting from exertional dyspnoea are common in people with COPD. Dyspnoea includes multidimensional domains of intensity, unpleasantness and sensory descriptors. Low viscosity Helium gas mixes are known to improve exercise tolerance, but their effect on multidimensional domains of exertional dyspnoea in people with COPD are unknown. Aim To determine if a mixture of 79:21 Helium:Oxygen (Heliox [HeO2]) alters the perception of exertional dyspnoea during constant work rate (CWR) exercise in people with COPD. Methods Using a randomized, double blind, cross over design, participants completed four cycle ergometry tests including, an incremental symptom limited test to WRmax, a familiarisation CWR (at 60% WRmax) test on medical air (MA) and CWR tests on HeO2 and MA. Gas mixes were administered via closed breathing circuits. On large format charts, participants indicated VAS ratings (intensity, unpleasantness) and applicable descriptors every two minutes. VAS ratings and descriptors were analysed using random effects mixed modelling. Results 14 people (11 male; mean age 69.3±6.6 yrs, FEV1 39±9 %pred) completed all sessions. Endurance time significantly increased under HeO2 conditions (9.3±9.2 vs. 14.3±9.1 min). Significantly slower rates of increase were observed under HeO2 conditions for both dyspnoea intensity (p = 0.04) and unpleasantness (p=0.03). The frequency with which the dyspnoea descriptors Air Hunger (p=0.04) and Work/Effort (p=0.003) were selected was also significantly lower under HeO2 conditions. Conclusion Heliox reduces unpleasant sensations of exertional dyspnoea thereby contributing to improved exercise tolerance in people with COPD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".