Low dynamic ventilatory reserve during exercise in pulmonary hypertension: physiological and sensory implications
Bibliographic record
Abstract
Background: Low (↓) dynamic ventilatory reserve (VRdyn) can expose demand-capacity mismatch, a key determinant of exertional dyspnoea. Excess ventilation (ventilation (VE)/CO2 output), potentially reducing VRdyn, is a marker of disease severity in pulmonary hypertension (PH). Aim: To contrast the ventilatory and sensory responses to exercise in patients with PH who show ↓VRdyn or not. Methods: 91 patients with arterial PH (63%) and chronic thromboembolic PH (37%) performed an incremental CPET with serial measurements of inspiratory capacity (IC). Values < lower limit of normal (<5th centile) at the highest equivalent work of 40 W defined ↓VRdyn. Results: Patients with ↓VRdyn (54/91=59%; 59.1%♀; 47±15yrs, mPAP 50 mmHg) presented with worse NYHA class (2 [2-3] vs 1[1-2]), ↓6MWD (64 vs 81% pred), ↓ peak work rate (54 vs 68W), and mortality (20% vs 6%) than those with preserved VRdyn (p<0.05). Excess ventilation (panel A) – in addition to a trend to ↓ estimated MVV – led to ↓VRdyn (panel B), mechanical constraints (panels C-D), and dyspnoea vs work rate at submaximal exercise intensities (panel E) (p<0.05). In keeping with the central role of VE in eliciting dyspnoea, symptom intensity did not differ at iso-VE (panel F; p>0.05). erj;66/suppl_69/PA6284/F1 F1 F1 Conclusions: ↓VRdyn is a novel CPET variable signaling multiple interconnected clinically-relevant sensory and physiological abnormalities in patients with PH.
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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 | 0.000 | 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".