Severe exertional dyspnoea in COPD: Implications for exercise tolerance and survival
Bibliographic record
Abstract
Background: Age- and sex-based reference values for dyspnoea (0-10 Borg scale) as a function of ventilation (V’E) have been recently published (Neder et al.; ERJ 2020 1;56(4)). Increased dyspnoea-V’E may signal important lung mechanical abnormalities with potentially deleterious consequences for exercise tolerance and survival in COPD. Methods: 300 patients (172 men) with mild to very severe COPD underwent a cardiopulmonary exercise test (CPET). Dyspnoea scores above the 75th centile at the highest common V’E (25 L/min) indicated “severe” dyspnoea burden. All-cause mortality was assessed up to 20 years after the CPET. Results: 136 patients (45.3%) died: “severe” dyspnoea-V’E25 was found in 169/300 (56.3%). They presented with greater inspiratory constraints due to higher operating lung volumes at low V’E and poorer exercise tolerance compared to their counterparts (P<0.01). Kaplan-Meier analysis showed that, regardless of the GOLD stage, "severely" dyspnoeic patients had poorer survival (Figure). Stepwise Cox regression analysis revealed that “severe” dyspnoea-V’E25 predicted mortality in addition to V’E/CO2 output nadir>34 and peak work rate <45% predicted (P<0.001). Conclusion: Quantifying the intensity of submaximal dyspnoea-V’E during incremental CPET exposes critical mechanical abnormalities secondary to COPD whilst identifying the patients at a higher risk of poor survival across the spectrum of disease 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 | 0.001 |
| 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".