Classical electrical and hydraulic Windkessel models validate physiological calculations of Windkessel (reservoir) pressure
Bibliographic record
Abstract
Our "reservoir-wave approach" to arterial hemodynamics holds that measured arterial pressure should be considered to be the sum of a volume-related pressure (i.e., reservoir pressure, P(reservoir)) and a wave-related pressure (P(excess)). Because some have questioned whether P(reservoir) (and, by extension, P(excess)) is a real component of measured physiological pressure, it was important to demonstrate that P(reservoir) is implicit in Westerhof's classical electrical and hydraulic models of the 3-element Windkessel. To test the validity of our P(reservoir) determinations, we studied a freeware simulation of the electrical model and a benchtop recreation of the hydraulic model, respectively, measuring the voltage and the pressure distal to the proximal resistance. These measurements were then compared with P(reservoir), as calculated from physiological data. Thus, the first objective of this study was to demonstrate that respective voltage and pressure changes could be measured that were similar to calculated physiological values of P(reservoir). The second objective was to confirm previous predictions with respect to the specific effects of systematically altering proximal resistance, distal resistance, and capacitance. The results of this study validate P(reservoir) and, thus, the reservoir-wave approach.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".