The ABC Bicuspid Sizing Protocol for SAPIEN 3 Balloon-Expandable Valves
Bibliographic record
Abstract
Background: The computed tomography selection of patients with bicuspid aortic stenosis for treatment with balloon-expandable valve (BEV) transcatheter aortic valve replacement (TAVR) is uncertain. We therefore evaluated a novel sizing algorithm for SAPIEN 3 BEV. Methods: A prospective single-center registry from February 2020 to May 2024 including patients with bicuspid aortic stenosis treated with TAVR (and surgical aortic valve replacement starting in September 2022). TAVR patients were treated according to the ABC bicuspid sizing algorithm with criteria for annular area (<10% oversizing or <5% with calcification), below annular (left ventricular outflow tract [LVOT]) area (<10% oversizing or <5% with calcification), intercommissural distance (>planned valve diameter - 1 mm), maximum sinus diameter (>planned valve diameter + 6 mm), and avoidance of high-risk leaflet calcification. The study endpoints were technical success and device success at 30 days. Results: From February 2020 to May 2024, 106 patients were treated with TAVR, and (from September 2022) 10 patients were treated with surgical aortic valve replacement due to inadequate sinus dimensions for TAVR or the presence of high-risk calcium. Among TAVR-treated patients, final valve sizing was determined by annular area in 77.5%, LVOT in 21.7%, and intercommissural distance in 1.9%. Most changes (84%) involved reducing planned valve diameter, resulting in modest final annular (1.032%) and LVOT (1.042%) oversizing. Technical success was obtained in 105 (99%) patients, and device success at 30 days in 102 (96.2%) patients. Conclusions: A dedicated sizing algorithm for BEVs was able to select patients for BEV with very high device success.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.003 |
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".