P1.4 HEMODYNAMICS OF PULMONARY HYPERTENSION: APPLICATION OF THE RESERVOIR-WAVE APPROACH
Bibliographic record
Abstract
Using the reservoir-wave approach, previously we characterized pulmonary vasculature mechanics with multiple interventions in a canine model. In the present study, we measured high-fidelity pulmonary arterial (PA) pressure, Doppler flow velocity, and pulmonary capillary wedge pressure in 11 patients referred for evaluation of exertional dyspnea. The analysis was performed using the reservoir-wave approach; wave intensity analysis was subsequently utilized to characterize the PA wave pattern. Our objective was to identify specific abnormalities associated with pulmonary hypertension. Seven patients with varying PA pressures had reduced pulmonary vascular conductance (i.e., the amount of flow that the lungs can accept per pressure gradient), suggesting that these patients might benefit from pulmonary vasodilator therapy, some even in the absence of markedly elevated PA pressures. Right ventricular (RV) performance was assessed by examining the work done by the wave component of systolic PA pressure. Wave work, the non-recoverable energy expended by the RV to eject blood, varied directly with mean PA pressure. Wave pressure was partitioned into two components: forward-travelling and reflected backward-travelling waves. Among patients with lower PA pressures, we found pressure-decreasing backward waves that aided the RV during ejection, as previously reported in normal experimental animals. Among patients with higher PA pressures, we detected pressure-increasing backward waves that impede RV ejection. We conclude that it is important to measure pulmonary vascular conductance to properly assess the pulmonary vasculature. The reservoir-wave approach and wave intensity analysis may prove to be valuable tools to evaluate RV performance and may facilitate development of therapeutic strategies.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".