Resting lung volume phenotypes in COPD: implications for exertional dyspnoea and exercise tolerance
Bibliographic record
Abstract
Background: Lung volumes and dyspnoea vary markedly at a given forced expiratory volume in 1 s in COPD. We aim to investigate whether hyperinflation (high total lung capacity (TLC)) adds value to simpler inspiratory capacity (IC) in predicting mechanical-ventilatory impairment and exertional dyspnoea in these patients. Methods: 345 patients with mild to very severe COPD (190 men) underwent incremental cycling with measurements of dyspnoea (0-10 Borg) and operating lung volumes. Resting volumes by body plethysmography were compared with the 2021 z-score-based Global Lung Initiative standards. A novel artificial intelligence (AI)-based algorithm quantified the burden of mechanical-ventilatory constraints and dyspnoea as ventilation increased. Results: Four lung volume phenotypes were identified: 168 patients with preserved IC and TLC, 51 with preserved IC and high TLC (hyperinflation), 52 with low IC but no hyperinflation, and 74 with low IC and hyperinflation. Patients with low IC and/or hyperinflation showed worse air trapping and lower transfer factor (p<0.05). Hyperinflated patients at a given IC presented with worse sensory and functional outcomes; similarly, patients showing low IC at a given TLC were more symptomatic and impaired (p<0.05). The highest and lowest odds ratios (95% confidence interval) for "very severe" mechanical-ventilatory constraints and dyspnoea according to the AI algorithm were found in hyperinflated patients with low IC (5.2 (4.7-7.5)) and non-hyperinflated patients with preserved IC (0.99 (0.71-1.16)), respectively. Conclusion: By combining IC and TLC expressed as z-scores, clinicians can identify physiological phenotypes relevant to dynamic lung mechanical abnormalities on exertion, activity-related dyspnoea and exercise tolerance across the spectrum of COPD severity.
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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 | 0.003 |
| 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 | 0.001 | 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".