In Vitro Investigation of the Impact of Aortic Valve Stenosis Severity on Left Coronary Artery Flow
Bibliographic record
Abstract
Patients with aortic valve stenosis (AS) may experience angina pectoris even if they have angiographically normal coronary arteries. Angina is associated with a marked increase in the risk of sudden death in AS patients. Only a few in vitro models describing the interaction between the left ventricular and aortic pressures, and the coronary circulation have been reported. These models were designed for specific research studies and they need to be improved or modified when other specific studies are required. Consequently, we have developed an in vitro model that is able to mimic the coronary circulation in presence of aortic stenosis. First, we have validated the model under physiological conditions. Then, we have examined and quantified the hemodynamic effects of different degrees of AS (from normal to severe AS) on the coronary flow using a model of the normal left coronary artery. In the coronary in vitro model without AS (normal valve), the amplitude and shape of coronary flow were similar to those observed in in vivo measurements obtained under physiological conditions, as described by Hozumi et al. (1998, "Noninvasive Assessment of Significant Left Anterior Descending Coronary Artery Stenosis by Coronary Flow Velocity Reserve With Transthoracic Color Doppler Echocardiography," Circulation, 97, pp. 1557-1562). The presence of an AS induced an increase in the maximum and mean coronary flow rates (97% and 73%, respectively, for a very severe AS). Furthermore, when AS was very severe, a retrograde flow occurred during systole. This study allowed us to validate our coronary in vitro model under physiological conditions, both in the absence and presence of AS. These changes could explain the fact that even if patients have angiographically normal epicardial coronary arteries, we can observe the occurrence of angina pectoris in these patients in the presence of an AS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".