Implications of hydrodynamic testing to guide sizing of self‐expanding transcatheter heart valves for valve‐in‐valve procedures
Bibliographic record
Abstract
AIMS: The commonly used valve-in-valve (VIV) app recommends sizing based on dimensions of both the transcatheter heart valve (THV) and bioprosthetic surgical valve. The implications of hydrodynamic testing to guide VIV sizing are poorly understood. This bench study assessed the hydrodynamic performance of different sizes of self-expanding supra-annular THVs in three different surgical aortic bioprostheses at different implantation depths. METHODS: A small versus medium ACURATE neo (ACn), and a 26 mm versus 29 mm Evolut R were assessed after VIV implantation in 25 mm Mitroflow, Mosaic, and Magna Ease aortic surgical bioprostheses, at three implantation depths (+2 mm, -2 mm, and -6 mm). RESULTS: The medium-sized ACn had lower gradients compared to the small ACn when the THV was implanted high (+2 mm, or -2 mm). The 29 mm Evolut R had lower gradients compared to a 26 mm Evolut R for all implantation depths, except for a depth of -2 mm in the 25 mm Mitroflow. The medium ACn and 29 mm Evolut R had larger effective orifice areas compared to the small ACn and 26 mm Evolut R, respectively. Both Evolut R sizes had acceptable regurgitant fractions (<15%), while both ACn sizes were above the acceptable performance criteria (>15%), at all implantation depths. CONCLUSIONS: Use of a larger self-expanding THV was associated with superior hydrodynamic performance if the THV was implanted high. Hydrodynamic testing can provide additional information to the VIV app to help guide VIV sizing.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".