Vascular Compromise and Hemodynamics in Pulmonary Arterial Hypertension: Model Predictions
Bibliographic record
Abstract
A previously validated computer model of the normal pulmonary circulation is adapted to simulate pulmonary arterial hypertension (PAH) in humans. Model predictions are used to explore the suitability of currently accepted criteria for diagnosing PAH by correlating hemodynamic data with the degree of vascular compromise (disease severity). Model predictions demonstrate a hyperbolic relationship between vascular compromise, mean pulmonary artery pressure (PAPm) and pulmonary vascular resistance (PVR). PAPm and PVR change very little from disease initiation until a vascular compromise of 65% to 70% (surface area of 0.35 to 0.3 of baseline, respectively) is reached. Following that, further compromise is associated with a steep rise in PAPm and PVR. The relationship between vascular compromise and hemodynamics may explain the relative stability of cardiac output early in this disease process and, therefore, the lack of symptoms. It also explains the rapid deterioration following diagnosis if the disease remains untreated. Model predictions demonstrate the inadequacy of the current hemodynamic criteria for diagnosing PAH over a wide range of left atrial pressure and cardiac output combinations and for early detection of disease. The model provides an alternative approach to diagnosing PAH by translating hemodynamic data to degree of vascular compromise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".