Contrasting dynamic and peak ventilatory reserve during incremental CPET in pulmonary hypertension
Bibliographic record
Abstract
Background: Decrements (↓) in dynamic (dyn) ventilatory reserve (VR=[1-(ventilation/estimated MVV)] x 100) as exercise progresses may be helpful to expose increased demand-capacity mismatch, a key underpinning of exertional dyspnoea in cardiopulmonary diseases. Aim: To contrast the current criterion to detect ventilatory limitation to exercise - ↓ peak VR – against VRdyn in pulmonary hypertension (PH). Methods: 91 patients (66♀, 45-84yrs) with arterial PH (63%) or chronic thromboembolic PH performed an incremental CPET. ↓VRpeak was defined according to different thresholds (15%, 20%, and 30%). VRdyn<5th centile at 40W and dyspnoeadyn>95th centile considering all exercise intensities (DyVe-X software) defined an abnormal response. Results: A significant fraction of patients with preserved (↔) VRpeak (37/91= 41%) had ↓ VRdyn and ↑ dyspnoeadyn (Figure A). These subjects showed lower exercise capacity than those with ↔ VRdyn and/or ↔ dyspnoeadyn (p<0.05). Regardless of the metric and patient group, ↔VRpeak related poorly to dyspnoeadyn; in contrast, ↓VRdyn was associated with dyspnoeadyn and severely reduced exercise tolerance (p<0.01; Figure B). ↓VRdyn – but not ↓VRpeak – strongly predicted these outcomes (OR 42 (9-199) and 5 (2-15), respectively; p<0.01). erj;66/suppl_69/PA6292/F1 F1 F1 Conclusions: VRdyn improves the yield of CPET by exposing a link between excessive ventilation and an important patient-centered outcome in PH: exertional dyspnoea.
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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 | 0.002 |
| 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 | 0.000 | 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".