Distribution of C‐arm projections in native and bioprosthetic aortic valves cusps: Implication for BASILICA procedures
Bibliographic record
Abstract
OBJECTIVES: We sought to document aortic cusps fluoroscopic projections and their distributions using leaflet alignment which is a novel concept to optimize visualization of leaflets and for guiding BASILICA (bioprosthetic or native aortic scallop intentional laceration to prevent coronary artery obstruction) and determine whether these projections were feasible in catheter laboratory. BACKGROUND: Optimal fluoroscopic projections of aortic valve cusps have not been well described. METHODS: A total of 128 pre-transcatheter aortic valve replacement (pre-TAVR) computed tomographies (CT) (72 native valves and 56 bioprosthetic surgical valves) were analyzed. Using CT software (3Mensio, Pie medical imaging, the Netherlands), leaflet alignment was performed and the feasibility of these angles, which were defined as rate of obtainable with efforts (within LAO/RAO of 85° and CRA/CAU of 50°) were evaluated. RESULTS: High feasibility was seen in right coronary cusp (RCC) front view (100%) and left coronary cusp (LCC) side view (99.2%), followed by noncoronary cusp side view (95.3%). In contrast, low feasibility of RCC side view (7.8%) and LCC front view (47.6%) was observed. No statistical differences were seen between the distribution of native valves and bioprosthetic surgical valves. With patient/table tilt of 20°LAO and 10°CRA, the feasibility of RCC side view and LCC front view increased to 43.7 and 85.2%, respectively. CONCLUSION: Distributions of each cusp's leaflet alignment follows "sigmoid curve" which can provide better understanding of aortic valve cusp orientation in TAVR and BASILICA. RCC side view used in right cusp BASILICA is commonly unachievable in catheter laboratory and may improve with patient/table tilt.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".