Cardiac computed tomography and computed tomography angiography in the evaluation of patients prior to transcatheter aortic valve implantation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Advancements in the use of multidetector computed tomography (CT) in transcatheter aortic valve implantation/transcatheter aortic valve replacement (TAVI/TAVR) over the last year reaffirm its role in the evaluation of preprocedural planning and procedural guidance. The purpose of this review is to provide an up-to-date review of recently published data, with a particular focus on annular sizing and transcatheter heart valve selection to help reduce paravalvular regurgitation. RECENT FINDINGS: Recent data have confirmed that multidetector computed tomography (MDCT) measures of the annulus are highly reproducible across multiple readers and workstation platforms. MDCT has also been shown to have a strong discriminatory ability to predict and reduce postprocedural paravalvular regurgitation (PAR), as well as presenting the current data for integrating CT measures of the annulus into sizing. SUMMARY: Over the last year, MDCT has solidified itself as an essential tool for the evaluation of the aortic root and annulus prior to TAVI. MDCT annular measurements are highly reproducible and now form the basis for transcatheter heart valve selection, with early data suggesting that CT integration can reduce paravalvular regurgitation.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".