Cardiac Computed Tomography and Magnetic Resonance Imaging in the Evaluation of Mitral and Tricuspid Valve Disease
Bibliographic record
Abstract
Transcatheter interventions to treat mitral and tricuspid valve disease are becoming increasingly available because of the growing number of elderly patients with significant comorbidities or high operative risk. Thorough clinical and imaging evaluation in these patients is essential. The latter involves both characterization of the mechanism and severity of valvular disease as well as determining the hemodynamic consequences and extent of ventricular remodeling, which is an important predictor of future outcomes. Moreover, an assessment of the suitability and risk of complications associated with device-specific therapies is also an important component of the preprocedural evaluation in this cohort. Although echocardiography including 2-dimensional and 3-dimensional methods has an important role in the initial assessment and procedural guidance, cross-sectional imaging, including both computed tomographic imagning and cardiac magnetic resonance imaging, is increasingly being integrated into the evaluation of mitral and tricuspid valve disease. In this review, we discuss the role of cross-sectional imaging in mitral and tricuspid valve disease, primarily valvular regurgitation assessment, with an emphasis on the preprocedural evaluation and implications for transcatheter interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".