The importance of tissue science and valve design in relation to durability and hemodynamics of the DurAVR aortic heart valve
Bibliographic record
Abstract
Introduction: Clinical evidence highlighting the efficacy and safety of transcatheter aortic valve replacement (TAVR) and the 2019 Food and Drug Administration (FDA) approval for TAVR in low-risk (younger) patients has created a demand for durable and long-lasting bioprosthetic heart valve (BHV) leaflet materials. Over the life of an implanted BHV mechanical stress, immunogenicity, calcification, and hemodynamic dysfunction lead to failure via structural valve deterioration (SVD). Consequently, the durability of the bioprosthetic materials selected for valve manufacture is of utmost importance. Technology: The ADAPT™ tissue engineering process, an anti-calcification preparation that transforms xenograft tissue (bovine pericardium) into a durable valve bioscaffold, shows significant clinical benefits in mitigating the interrelated mechanisms leading to SVD. The novel acellular, biostable and non-calcifying biomaterial has recently been molded into a single-piece 3D biomimetic valve (DurAVR™) with excellent early clinical results and the potential to meet the growing demand of durable BHVs for the treatment of aortic stenosis. Discussion: The unique design of the DurAVR biomimetic valve in combination with the superior biostability of ADAPT tissue could advance the BHV space by providing superior performance and durability to aortic stenosis patients in need of TAVR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".