Neurophysiological mechanisms of exertional dyspnoea in fibrotic interstitial lung disease
Bibliographic record
Abstract
Our understanding of the mechanisms of dyspnoea in fibrotic interstitial lung disease (ILD) is incomplete. The aims of this study were two-fold: 1) to determine whether dyspnoea intensity is better predicted by neural respiratory drive (NRD) or neuromechanical uncoupling (NMU) of the respiratory system in fibrotic ILD, and 2) to examine the effect of breathing 60% oxygen on NRD, NMU and dyspnoea ratings.Fourteen patients with fibrotic ILD were included. Visit 1 comprised a familiarisation incremental cycle exercise test, Visit 2 comprised a normoxic incremental cycling test to address Aim 1, and Visits 3 and 4 consisted of constant-load cycling while breathing room air or 60% oxygen to address Aim 2. Diaphragmatic electromyography (EMGdi) was used as a surrogate of NRD. NMU was calculated as the ratio between EMGdi (%max) and tidal volume (%vital capacity).On adjusted analysis, NMU and its constituents were all significantly associated with dyspnoea ratings during incremental cycling, with EMGdi having the strongest correlation. The between-treatment change in dyspnoea ratings during constant load cycling was only correlated with change in exercise endurance time and NMU.Dyspnoea more strongly reflected the level of EMGdi than NMU in fibrotic ILD. However, the improvement in dyspnoea with 60% oxygen was better predicted by improvements in NMU.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".