Using AI to expose the mechanisms of exertional dyspnoea in COPD: conflating “excessive” and “constrained” ventilation during incremental CPET
Bibliographic record
Abstract
Background: There is a need of novel paradigms to interpret cardiopulmonary exercise testing (CPET) which properly considers the neurobiological underpinnings of exertional dyspnoea. Aim: We tested a conceptual framework which relates complementary metrics of demand-capacity imbalance of the respiratory system with the severity of dyspnoea as exercise intensifies. Methods: An AI-based software (DyVe-X) determined the overall burden (i.e., considering all CPET data points) of sex- and age-adjusted metrics of “excessive ventilation” (↓ dynamic ventilatory reserve (VRdyn)), “constrained ventilation” (↓ dynamic inspiratory reserve based on tidal volume/inspiratory capacity (IRdyn1) or inspiratory reserve volume/total lung capacity (IRdyn2)), and ↑ dyspnoea (Borg 0-10) in 359 men and women with mild to end-stage COPD Results: Severe decrements (<25th centile) in VRdyn, IRdyn1, and IRdyn2 showed high positive predictive values for “intense” dyspnoea-work rate (>75th centile); p<0.001)). Binary logistic regression, showed that IRdyn2 added information to the simpler IRdyn1 and VRdyn to predict “intense” dyspnoea-work rate (odds ratio (95% CI)= 3.26 (1.09-9.74), 21.6 (4.7-92.9), and 10.4 (3.03-35.6) respectively; 86.1 % correct). Of note, IRdyn1 and IRdyn2 – but not VRdyn- were highly predictive of “intense" dyspnea-ventilation (22.8 (7.37-70.7) and 9.6 (3.1-29.7) respectively; 89.1 % correct), i.e., "constrained ventilation". Conclusions: By combining indexes of “excessive” and “constrained” ventilation throughout incremental CPET, DyVe-X may provide unique insights into the physiological underpinnings of exertional dyspnoea in individual patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".