Dynamic interactions between submaximal leg effort and dyspnoea during incremental CPET: implications for exercise tolerance in COPD
Bibliographic record
Abstract
Background: Adding the severity of peripheral and respiratory symptoms across submaximal exercise intensities may enhance our ability to predict exercise tolerance in COPD. Aim: To determine the best approaches to quantify the cumulative burden of activity-related symptoms vis-à-vis peak work rate (WR) and O2 uptake (VO2) in COPD of varied severity. Methods: An AI-based software (DyVe-X) classified the dynamic burden of CR10 Borg leg effort and dyspnoea across increasing work rates during cardiopulmonary exercise testing (CPET) in 354 patients. These metrics were compared with peak symptom burden (“mild”=both scores≤2, “severe”=both>5 and “moderate”= any other combination). Results: Neither peak WR nor peak VO2 differed between patients according to the combined peak symptom burden (p>0.05). Conversely, both variables progressively decreased with the severity of dynamic symptom burden (p<0.001; Figure). Controlling for COPD stage, “severe” dynamic symptom burden -but not “severe” peak symptom burden – predicted “severe” impairment in peak WR (<50% pred; OR (95% CI)= 3.69 (2.36-5.76)) and peak VO2 (<60 % pred; 3.56 (2.23-5.68))(p<0.001), erj;64/suppl_68/PA1676/F1 F1 F1 Conclusions: Combining the dynamic assessment of leg effort and dyspnoea severity throughout incremental CPET strongly predicts peak exercise capacity in COPD. Both symptoms should be addressed to enhance exercise tolerance in this patient population.
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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 | 0.000 | 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) | 0.003 | 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".