A novel anatomic classification to guide transcatheter aortic valve replacement for pure aortic regurgitation
Bibliographic record
Abstract
BACKGROUND: The success of transcatheter aortic valve replacement (TAVR) in native aortic regurgitation (AR) is limited by the absence of calcified anchoring structures. We sought to evaluate transfemoral TAVR in patients with native AR using a novel aortic root imaging classification. METHODS: From March to November 2021, 81 patients with severe AR were prospectively enrolled in 2 cardiac centers in China. All were evaluated using multidetector computed tomography (MDCT) and classified into 4 anatomic types in reference to transcatheter heart valve (THV) anchoring: Type 1: anchoring at the left ventricular outflow tract (LVOT), annulus, and ascending aorta (AA); Type 2: anchoring at the annulus and AA; Type 3: anchoring at the annulus and LVOT; and Type 4: anchoring at only 1 level or none at all. Based on the dual-anchoring strategy, patients with Types 1-3 were considered TAVR candidates. Procedural and 30-day outcomes were assessed according to Valve Academic Research Consortium-3 definitions. RESULTS: TAVR was performed in 32 (39.5%) patients (71.9 ± 8.0 years of age, 71.9% were male) using 2 self-expanding THVs. Types 1, 2, and 3 comprised 13 (40.6%), 11 (34.4%), and 8 (25.0%) cases, respectively. The procedural and device success rates were 100% and 93.8%, respectively, with 2 THV migration. Eight patients (25.0%) required a permanent pacemaker, and 2 (6.3%) developed moderate paravalvular leaks. No deaths or other major complications occurred during the study. CONCLUSIONS: The novel anatomic classification and dual-anchoring strategy were associated with a high procedural success rate with favorable short-term safety and clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".