CT in Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Transcatheter aortic valve replacement is a new method to treat patients with symptomatic, severe aortic stenosis who are at high surgical risk. Short- and midterm results have been encouraging, with more than 90,000 procedures performed worldwide. Patient selection, prosthesis sizing, and access strategies heavily rely on noninvasive imaging. Computed tomographic (CT) angiography is increasingly used for peri-interventional evaluation, as this modality allows for objective three-dimensional assessment of the aortic root, evaluation of the iliofemoral access route, and prediction of appropriate projection angles for prosthesis deployment. Compared with two-dimensional imaging techniques, CT provides comprehensive information about aortic annulus anatomy and geometry, supporting appropriate patient selection and prosthesis sizing. Recently, integration of CT measurements into sizing algorithms has been demonstrated to significantly reduce the incidence of paravalvular regurgitation, compared with prosthesis sizing with two-dimensional echocardiography. In addition, CT-based vascular access planning has been shown to reduce vascular access complications. Postprocedural CT imaging allows for the documentation of procedural success, evaluation of prosthesis positioning, and identification of asymptomatic complications. In this article, the rapidly emerging role of CT in the context of transcatheter aortic valve replacement will be described. Online supplemental material is available for this article.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".