In vitro evaluation of implantation depth in valve-in-valve using different transcatheter heart valves
Bibliographic record
Abstract
AIMS: Transcatheter heart valve (THV) implantation in failed bioprosthetic valves (valve-in-valve [ViV]) offers an alternative therapy for high-risk patients. Elevated post-procedural gradients are a significant limitation of aortic ViV. Our objective was to assess the relationship between depth of implantation and haemodynamics. METHODS AND RESULTS: Commercially available THVs used for ViV were included in the analysis (CoreValve Evolut, SAPIEN XT and the Portico valve). THVs were implanted in small surgical valves (label size 19 mm) to simulate boundary conditions. Custom-mounted pulse duplicators registered relevant haemodynamic parameters. Twenty-eight experiments were performed (13 CVE, 5 SXT and 10 Portico). Ranges of depth of implantation were: CVE: -1.2 mm to 15.7 mm; SXT: -2.2 mm to 7.5 mm; Portico: 1.4 mm to 12.1 mm. Polynomial regression established a relationship between depth of implantation and valvular mean gradients (CVE: p<0.001; SXT: p=0.01; Portico: p=0.002), as well as with EOA (CVE: p<0.001; SXT: p=0.02; Portico valve: p=0.003). In addition, leaflet coaptation was better in the high implantation experiments for all valves. CONCLUSIONS: The current comprehensive bench testing assessment demonstrates the importance of high device position for the attainment of optimal haemodynamics during aortic ViV procedures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".