Novel paradigms for clinical CPET interpretation: dynamic assessment of dyspnoea and ventilation during exercise (DyVe-X)
Bibliographic record
Abstract
Background: Current cardiopulmonary exercise testing (CPET) interpretation approaches are largely insensitive to a key underpinning of exertional dyspnoea: dynamic demand-capacity imbalance. Aim: To develop novel CPET data analysis and interpretation software linking heightened mechanical-ventilatory demands relative to capacity and exertional dyspnoea throughout incremental CPET. Methods: AI-based software (DyVe-X) used loss function for classification to determine the severity of dyspnoea (Borg 0-10) and ventilatory constraints considering all CPET data points in 359 men and women with mild to end-stage COPD. DyVe-X output was compared with the traditional approach to indicate ventilatory limitation: peak ventilatory reserve<15%. Results: Dyspnoea-work rate and dyspnoea-ventilation increased with the severity of submaximal ventilatory constraints as indicated by DyVe-X. ∼ 50% of patients with preserved peak ventilatory reserve showed submaximal ventilatory constraints; ∼ 90% of them showed very severe dyspnoea-ventilation (> 95th centile of age- and sex-adjusted standards). Regardless of peak ventilatory reserve, patients showing submaximal ventilatory constraints had lower exercise capacity compared with non-constrained patients (p<0.05) (Figure). erj;66/suppl_69/PA6282/F1 F1 F1 Conclusion: DyVe-X is poised to improve CPET yield by exposing a role for "the lungs" in eliciting exertional dyspnoea in clinical populations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".