Mechanical-ventilatory responses and exertional dyspnoea in ever-smokers at risk for COPD in CanCOLD: an AI-based study
Bibliographic record
Abstract
Background: Continuous, dynamic (dyn) assessment of mechanical-ventilatory responses during incremental CPET may detect respiratory abnormalities relevant to dyspnoea in ever-smokers at risk for COPD. Aim: To contrast the current key CPET criterium to establish ventilatory limitation – peak ventilatory reserve (VRpeak)<15% – versus a novel AI-based approach (DyVe-X) which considers on data points in ever-smokers evaluated in the CanCOLD study. Methods:DyVe-X determined the severity of submaximal ventilatory (VRdyn) and mechanical (dynamic inspiratory reserve (IRdyn)) constraints and dyspnoeadyn (Borg 0-10) during incremental CPET in 443 ever smokers (282M; median [IQR]=15[26.5] pack-yrs) without airflow obstruction (post-BD FEV1/FVC>-1.645 z-score). Results:∼30% of subjects with preserved (⇔) or reduced (⇓) VRpeak were DyVe-X(+) or DyVe-X(-), respectively (p<0.001; McNemar test). Dyspnoeadyn severity (Figure 1A) and exercise capacity (Figure 1B) were more closely related to DyVe-X results than VRpeak categories (p<0.05). Logistic regression revealed that VRdyn (OR [95%CI]=2.12 [1.34-3.37] and IRdyn (1.57 [1.07-2.47]) – but not VRpeak – predicted “high” dyspnoeadyn (>75th centile) (p<0.001). erj;66/suppl_69/PA5189/F1 F1 F1 Conclusions: DyVE-X – an AI-based CPET interpretation algorithm – is superior to the traditional VRpeak in detecting physiological abnormalities germane to exertional dyspnoea in ever-smokers at risk for COPD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".