Numerical method to measure velocity integration, stroke volume and cardiac output while rest: using 2D fluid-solid interaction model
Bibliographic record
Abstract
Development of knowledge of cardiovascular diseases and treatments strongly depends on understanding of hemodynamic measurements.Hemodynamic parameters, therefore, have been investigated using simulation-based methods.A two-dimensional model was applied for seven healthy subjects with echo-Doppler at rest.Echocardiography imaging was also utilized to gain the geometry of the aortic valve.Fluid-Structure Interaction (FSI) model was carried out, coupling an Arbitrary Lagrangian-Eulerian mesh.Pressure loads were used as boundary conditions on the valve's ventricular and aortic sides.Pressure loads used were the calculated brachial pressures plus differences between brachial, central and left ventricular pressures.The FSI model predicted the velocity integration, stroke volume and cardiac output over a range of heart rates while rest.Numerical results generally had a difference of 5.4 to 15.87% with Doppler results.Linear correlations between numerical and clinical approaches have been applied.This makes possible predictions achieved from the FSI model to be gained which are highly accurate (e.g.correlation factor r = 0.995, 0.990 and 0.990 for velocity integration, stroke volume and cardiac output, respectively).The obtained numerical results showed that numerical methods can be combined with clinical measurements to provide good estimates of patient specific hemodynamics for different subjects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".