Safety and efficacy of transcatheter aortic valve implantation in stenotic bicuspid aortic valve compared to tricuspid aortic valve: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve implantation (TAVI) has emerged as a safe and effective alternative to surgical replacement for tricuspid aortic valve (TAV) stenosis. However, utilization of TAVI for aortic stenosis in bicuspid aortic valve (BAV) compared to TAV remains controversial. METHODS: We queried online databases with various keywords to identify relevant articles. We compared major cardiovascular events and procedural outcomes using a random effect model to calculate odds ratios (OR). RESULTS: We included a total of 22 studies comprising 189,693 patients (BAV 12,669 vs. TAV 177,024). In the pooled analysis, there were no difference in TAVI for BAV vs. TAV for all-cause mortality, cardiovascular mortality, myocardial infarction (MI), vascular complications, acute kidney injury (AKI), coronary occlusion, annulus rupture, and reintervention/reoperation between the groups. The incidence of stroke (OR 1.24; 95% CI 1.1-1.39), paravalvular leak (PVLR) (OR 1.42; 95% CI 1.26-1.61), and the need for pacemaker (OR 1.15; 95% CI 1.06-1.26) was less in the TAV group compared to the BAV group, while incidence of life-threatening bleeding was higher in the TAV group. Subgroup analysis mirrored pooled outcomes except for all-cause mortality. CONCLUSION: The use of TAVI for the treatment of aortic stenosis in selective BAV appears to be safe and effective.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".