The link between elevated inspiratory neural drive, inspiratory constraints, and dyspnea across the continuum of COPD severity
Bibliographic record
Abstract
Background: Exertional dyspnea in patients with chronic obstructive pulmonary disease (COPD) is associated with high inspiratory neural drive (IND). The contribution of critical inspiratory constraints (CIC) to high IND across the COPD severity continuum is unknown. Aims: To compare dyspnea, IND and CIC during exercise in mild to advanced COPD. Methods: A two-centre, cross-sectional study in which 18 controls (C) and 90 COPD participants, grouped in tertiles of FEV1%predicted (T1:87±9%; T2:60±9%; T3:32±8%), completed cycle exercise with measurement of IND by diaphragm electromyography (EMGdi (%max)) and CIC by tidal volume/inspiratory capacity (VT/IC). Results: IND and dyspnea during exercise were progressively elevated from C to T3 in conjunction with worsening resting and dynamic mechanics. CIC (VT/IC>70%) occurred at progressively lower ventilation (V̇E) from C to T3, which was associated with progressively lower resting IC (r=0.57, p<0.01). Strong associations were identified between VT/IC, IND and dyspnea during exercise at V̇E=25 L/min (Fig A). The close relationship between IND and both dyspnea (Fig B) and VT/IC (Fig C) were similar between groups, despite major differences in resting and dynamic mechanics. Conclusions: Exertional dyspnea in COPD was mechanistically linked to abnormally elevated IND, which, in turn, was strongly influenced by CIC in mild to advanced COPD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".