An In Vitro Model of Aortic Stenosis for the Assessment of Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
A significant number of elderly patients with severe symptomatic aortic stenosis are denied surgical aortic valve replacement (SAVR) because of high operative risk. Transcatheter aortic valve implantation (TAVI) has emerged as a valid alternative to SAVR in these patients. One of the main characteristics of TAVI, when compared to SAVR, is that the diseased native aortic valve remains in place. For hemodynamic testing of new percutaneous valves and clinical training, one should rely on animal models. However, the development of an appropriate animal model of severe aortic stenosis is not straightforward. This work aims at developing and testing an elastic model of the ascending aorta including a severe aortic stenosis. The physical model was built based on a previous silicone model and tested experimentally in this study. Experimental results showed that the error between the computer-aided design (CAD) file and the physical elastic model was <5%, the compliance of the ascending aorta was 1.15 ml/mm Hg, the effective orifice area (EOA) of the stenotic valve was 0.86 cm2, the peak jet velocity was 4.9 m/s and mean transvalvular pressure gradient was 50 mm Hg, consistent with as severe. An EDWARDS-SAPIEN 26 mm valve was then implanted in the model leading to a significant increase in EOA (2.22 cm2) and a significant decrease in both peak jet velocity (1.29 m/s) and mean transvalvular pressure gradient (3.1 mm Hg). This model can be useful for preliminary in vitro testing of percutaneous valves before more extensive animal and in vivo tests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".