The Paradox of Pulmonary Vascular Resistance: Restoration of Pulmonary Capillary Recruitment as a Sine Qua Non for True Therapeutic Success in Pulmonary Arterial Hypertension
Bibliographic record
Abstract
Exercise-induced increases in pulmonary blood flow normally increase pulmonary arterial pressure only minimally, largely due to a reserve of pulmonary capillaries that are available for recruitment to carry the flow. In pulmonary arterial hypertension, due to precapillary arteriolar obstruction, such recruitment is greatly reduced. In exercising pulmonary arterial hypertension patients, pulmonary arterial pressure remains high and may even increase further. Current pulmonary arterial hypertension therapies, acting principally as vasodilators, decrease calculated pulmonary vascular resistance by increasing pulmonary blood flow but have a minimal effect in lowering pulmonary arterial pressure and do not restore significant capillary recruitment. Novel pulmonary arterial hypertension therapies that have mainly antiproliferative properties are being developed to try and diminish proliferative cellular obstruction in precapillary arterioles. If effective, those agents should restore capillary recruitment and, during exercise testing, pulmonary arterial pressure should remain low despite increasing pulmonary blood flow. The effectiveness of every novel therapy for pulmonary arterial hypertension should be evaluated not only at rest, but with measurement of exercise pulmonary hemodynamics during clinical trials.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".