Partitioning pulmonary vascular resistance using the reservoir-wave model
Bibliographic record
Abstract
The conventional determination of pulmonary vascular resistance does not indicate which vascular segments contribute to the total resistance of the pulmonary circulation. Using measurements of pressure and flow, the reservoir-wave model can be used to partition total pulmonary vascular resistance into arterial, microcirculation, and venous components. Changes to these resistance components are investigated during hypoxia and inhaled nitric oxide, volume loading, and positive end-expiratory pressure. The reservoir-wave model defines the pressure of a volume-related reservoir and the asymptotic pressure. The mean values of arterial and venous reservoir pressures and arterial and venous asymptotic pressures define a series of resistances between the main pulmonary artery and the pulmonary veins: the resistance of large and small arteries, the microcirculation, and veins. In 11 anaesthetized, open-chest dogs, pressure and flow were measured in the main pulmonary artery and a single pulmonary vein. Volume loading reduced each vascular resistance component, whereas positive end-expiratory pressure only increased microcirculation resistance. Hypoxia increased the resistance of small arteries and veins, whereas nitric oxide only decreased small-artery resistance significantly. The reservoir-wave model provides a novel method to deconstruct total pulmonary vascular resistance. The results are consistent with the expected physiological responses of the pulmonary circulation and provide additional information regarding which segments of the pulmonary circulation react to hypoxia and nitric oxide.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".