Preprocedural CT Evaluation of Transcatheter Aortic Valve Replacement: What the Radiologist Needs to Know
Bibliographic record
Abstract
Aortic valve stenosis is the most common valvular heart disease in the Western world. When symptomatic, aortic valve stenosis is a debilitating disease with a dismal short-term prognosis, invariably leading to heart failure and death. Elective surgical valve replacement has traditionally been considered the standard of care for symptomatic aortic valve stenosis. However, several studies have identified various subgroups of patients with a significantly elevated risk for surgery-related complications and death. Thus, not every patient is a suitable candidate for surgery. Recent developments in transcatheter-based therapies have provided an alternative therapeutic strategy for the nonsurgical patient population known as transcatheter aortic valve replacement (TAVR) (also called transcatheter aortic valve implantation or percutaneous aortic valve replacement). In TAVR, the native aortic valve is replaced with a bioprosthetic valve via a nonsurgical endovascular, transaortic, or transapical pathway. Nevertheless, several anatomic and technical criteria must be met to safeguard patient eligibility and procedural success. Therefore, noninvasive imaging plays a crucial role in both patient selection and subsequent matching to a specific transcatheter valve size in an effort to ensure accurate prosthesis deployment and minimize peri- and postprocedural complications. The authors review the relevant anatomy of the aortic root, emphasizing the implications of anatomic pitfalls for correct reporting of imaging-derived measurements and important differences between findings obtained with different imaging modalities. They also discuss the evolving role of computed tomography and the role of the radiologist in patient triage in light of current viewpoints regarding patient selection, device size selection, and the preprocedural evaluation of possible access routes. 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".