The reservoir-wave approach to characterize pulmonary vascular-right ventricular interactions in humans
Bibliographic record
Abstract
Using the reservoir-wave approach (RWA) we previously characterized pulmonary vasculature mechanics in a normal canine model. We found reflected backward-traveling waves that decrease pressure and increase flow in the proximal pulmonary artery (PA). These waves decrease right ventricular (RV) afterload and facilitate RV ejection. With pathological alterations to the pulmonary vasculature, these waves may change and impact RV performance. Our objective in this study was to characterize PA wave reflection and the alterations in RV performance in cardiac patients, using the RWA. PA pressure, Doppler-flow velocity, and pulmonary arterial wedge pressure were measured in 11 patients with exertional dyspnea. The RWA was employed to analyze PA pressure and flow; wave intensity analysis characterized PA waves. Wave-related pressure was partitioned into two components: pressures due to forward-traveling and to backward-traveling waves. RV performance was assessed by examining the work done in raising reservoir pressure and that associated with the wave components of systolic PA pressure. Wave-related work, the mostly nonrecoverable energy expended by the RV to eject blood, tended to vary directly with mean PA pressure. Where PA pressures were lower, there were pressure-decreasing/flow-increasing backward waves that aided RV ejection. Where PA pressures were higher, there were pressure-increasing/flow-decreasing backward waves that impeded RV ejection. Pressure-increasing/flow-decreasing backward waves were responsible for systolic notches in the Doppler flow velocity profiles in patients with the highest PA pressure. Pulmonary hypertension is characterized by reflected waves that impede RV ejection and an increase in wave-related work. The RWA may facilitate the 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".