Quantifying dynamic mechanical-ventilatory constraints and exertional dyspnoea during incremental CPET in fibrosing ILD
Bibliographic record
Abstract
Background: Mechanical-ventilatory constraints are key determinants of exertional dyspnoea in fibrosing interstitial lung disease (f-ILD). Assessing these abnormalities at peak CPET only may not accurately depict the dynamic (dyn) constraints as exercise evolves. Aim: To contrast novel paradigms for CPET data display and analysis (DyVe-X) with traditional metrics of ventilatory limitation – low (⇓) peak ventilatory reserve (VRpeak) - in f-ILD of varied severity, Methods: Using DyVe-X, we quantified the severity of ventilatory (⇓ dynamic VR (VRdyn)) and mechanical (⇓ dynamic inspiratory reserve (IRdyn)) constraints and exertional dyspnoea (⇑ Borg 0-10 scores) during incremental CPET in 95 patients (68 men, DLCO 18-91% predicted). Results:∼50 % with preserved (⇔) VRpeak showed abnormal DyVe-X findings. Patients showing higher mechanical ventilatory constraints and dyspnoea on DyVe-X (>75th centile) - but not ⇓ VRpeak - had lower exercise capacity than less dyspnoeic patients with fewer constraints (p<0.01) (Figure). erj;66/suppl_69/OA5397/F1 F1 F1 Binary logistic regression revealed that ⇓ IRdyn (OR [95% CI]=6.46 [2.44-17.13]) and ⇓ VRdyn=5.56 [1.73-17.87]) - but not ⇓ VRpeak - were independently associated with ⇑ dyspnoea burden (p<0.001) Conclusion: Using DyVe-X, clinicians can improve the sensitivity of CPET in exposing submaximal mechanical-ventilatory constraints in dyspnoeic patients with mild to very severe f-ILD.
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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 | 0.000 | 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 | 0.000 |
| 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".