Transcatheter Aortic Valve Implantation for Bioprosthetic Valve Failure: Placement of Aortic Transcatheter Valves 3 Aortic Valve-in-Valve Study
Bibliographic record
Abstract
Background: Transcatheter aortic valve implantation is safe and effective for high-risk patients with bioprosthetic valve failure (BVF) but has not been studied in low- and intermediate-risk patients. One year outcomes of the PARTNER 3 Aortic Valve-in-valve (AViV) Study were evaluated. Methods: This prospective, single-arm, multicenter study enrolled 100 patients from 29 sites with surgical BVF. The primary endpoint was a composite of all-cause mortality and stroke at 1 year. The key secondary outcomes included mean gradient, functional capacity, and rehospitalization (valve-related, procedure-related, or heart failure related). Results: A total of 97 patients underwent AViV with a balloon-expandable valve from 2017 to 2019. Patients were 79.4% male with a mean age of 67.1 years and Society of Thoracic Surgeons score of 2.9%. The primary endpoint occurred in 2 patients (2.1%) who had strokes; there was no mortality at 1 year. Five patients (5.2%) had valve thrombosis events, and 9 patients (9.3%) had rehospitalizations, including 2 (2.1%) for strokes, 1 (1.0%) for heart failure, and 6 (6.2%) for aortic valve reinterventions (3 explants, 3 balloon dilations, and 1 percutaneous paravalvular regurgitation closure). From baseline to 1 year, New York Heart Association class III/IV decreased from 43.3% to 4.5%, mean gradient from 39.1 ± 18.2 mm Hg to 19.7 ± 7.6 mm Hg, and ≥moderate aortic regurgitation from 41.1% to 1.1%. Conclusions: AViV with a balloon-expandable valve improved hemodynamic and functional status at 1 year and can provide an additional therapeutic option in selected low- or intermediate-risk patients with surgical BVF, although longer term follow-up is necessary.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".