Aortic Stenosis and Systemic Hypertension, Modeling of
Bibliographic record
Abstract
Abstract Aortic stenosis is the most common cardiovascular disease after systemic hypertension and coronary artery disease in developed countries. It induces an obstruction to blood flow from the left ventricle to the aorta resulting in an increase in left ventricular afterload. More than 30% of patients with aortic stenosis have concomitant systemic hypertension. In such patients, the left ventricle faces a double pressure overload (valvular and vascular). A detailed understanding of the respective impacts of aortic stenosis and hypertension on left ventricular function would help to better predict whether aortic valve replacement and/or antihypertensive medical treatment would be beneficial. To better understand how coexisting aortic stenosis and hypertension affect the left ventricular function, we developed a relatively simple mathematical cardiovascular model to simulate the ventricular‐valvular‐vascular hemodynamic interaction(V3model). The present chapter provides a detailed description of the V3model along with numerical findings as well as their clinical implications. Several simulations with the V3model were performed to describe the effect of aortic stenosis on left ventricular stroke work and show the effect of coexistent systemic hypertension. Our simulations demonstrated that mild or moderate aortic stenosis has a small impact on left ventricular stroke work, whereas the latter increases noticeably when aortic stenosis becomes severe. They also showed that even mild hypertension may greatly influence left ventricular stroke work in patients with aortic stenosis. The mathematical V3model thus provides a potentially useful tool to investigate complex cardiovascular interactions that could be of great clinical interest.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".