Exertional ventilatory and gas exchange inefficiency: contrasting interstitial versus pulmonary vascular disease
Bibliographic record
Abstract
High (⇑) ventilation (V̇E)/carbon dioxide output (V̇CO2) ratio and low (⇓) end-tidal CO2 pressure (PETCO2) during exercise are expected in the presence of increased areas of ⇑ alveolar ventilation (V̇A) -capillary perfusion (Qc) relationship. Whether impaired Qc in isolation (i.e., pulmonary arterial hypertension (PAH)) furthers ⇑V̇E/V̇CO2 and ⇓PETCO2 beyond expectations from diseases affecting both V̇A and Qc (i.e.,interstitial lung disease (ILD)) remains unclear. Resting lung function and responses to symptom-limited cycling incremental cardiopulmonary exercise test were analysed in PAH patients compared to chronic fibrosing ILD. 36 PAH patients (40.8±12.9yrs;8♂), 39 ILD (60.8±10.1yrs;24) and 16 controls (51.1±14.7yrs;6♂) were included. DLCO (62±15 vs 40±14%pred;p<0.05) and peak exercise pulse oximetry (SpO2=93±6 vs 89±9%;p=0.09) were higher in PAH vs ILD. Exercise V̇E/V̇CO2NADIR was progressively higher from controls (25.3±3.5) to ILD (36.5±8.9) and PAH (41.9±7.4L/L) (all p<0.01). The inverse was found to the highest PETCO2 during exercise (Figure). Despite more preserved DLCO and ⇓ hypoxic drive, pulmonary vascular dysfunction had a larger negative influence on exertional ventilatory and gas exchange efficiency than ILD. These findings show that increases in V̇E/V̇CO2 and decreases in PETCO2 point to potential pulmonary vascular dysfunction, a common chronic cardiopulmonary disease comorbidity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".