Mechanics Post-TAVR for Trileaflet vs Bicuspid Aortic Valves
Bibliographic record
Abstract
BACKGROUND: Bicuspid aortic valve (BAV) disease is the most common congenital heart defect and is associated with elliptical openings and excess calcification. Limited data are available on long-term efficacy of transcatheter aortic valve replacement (TAVR) for BAV patients. OBJECTIVES: The purpose of this study was to compare simulated mechanical metrics post-TAVR between BAV and trileaflet patients with calcific aortic stenosis. METHODS: Pre-TAVR computed tomography scans of trileaflet (n = 22) and BAV (n = 25) were used to segment the aortic valve. Simulated balloon-expandable stent deployment was performed from a previously developed protocol. The bioprosthetic leaflets were then pressurized under a physiological pressure. Stent waist expansion ratio, aspect ratio (AR), leaflet systolic normalized geometric orifice area (GOA), and maximum leaflet diastolic stress were measured. RESULTS: BAVs had lower waist expansion ratio (87.8% ± 2.6% vs 89.4% ± 1.4%; P < 0.001), lower waist AR (94.6% ± 2.8% vs 98.2% ± 1.0%; P < 0.001), lower normalized GOA (88.9% ± 0.8% vs 89.5% ± 0.7%; P < 0.01), and higher maximum stress (2.81 ± 0.44 vs 2.29 ± 0.25 MPa; P < 0.001) on average compared to trileaflet patients, respectively. Within the BAV group, calcium volume was correlated with waist AR (r = -0.75, P < 0.001), normalized GOA (r = -0.68, P < 0.001), and maximum stress (r = 0.66, P < 0.001). CONCLUSIONS: Balloon-expandable TAVR for BAV has worse simulated mechanical metrics compared to TAVR for trileaflet aortic valve patients and is associated with high calcium volume.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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".