A Computational Study on Deformed Bioprosthetic Valve Geometries: Clinically Relevant Valve Performance Metrics
Bibliographic record
Abstract
Transcatheter aortic valves (TAV) are symmetrically designed, but they are often not deployed inside cylindrical conduits with circular cross-sectional areas. Many TAV patients have heavily calcified aortic valves, which often result in deformed prosthesis geometries after deployment. We investigated the effects of deformed valve annulus configurations on a surgical bioprosthetic valve as a model for TAV. We studied valve leaflet motions, stresses and strains, and analog hydrodynamic measures (using geometric methods), via finite element (FE) modeling. Two categories of annular deformations were created to approximate clinical observations: (1) noncircular annulus with valve area conserved, and (2) under-expansion (reduced area) compared to circular annulus. We found that under-expansion had more impact on increasing stenosis (with geometric orifice area metrics) than noncircularity, and that noncircularity had more impact on increasing regurgitation (with regurgitation orifice area metrics) than under-expansion. We found durability predictors (stress/strain) to be the highest in the commissure regions of noncircular configurations such as EllipMajor (noncircular and under-expansion areas). Other clinically relevant performance aspects such as leaflet kinematics and coaptation were also investigated with the noncircular configurations. This study provides a framework for choosing the most challenging TAV deformations for acute and long-term valve performance in the design and testing phase of device development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".