Randomized controlled trials in valvular heart disease: the evolving role of multimodality imaging
Bibliographic record
Abstract
Valvular heart disease represents a significant global health burden, with an estimated prevalence of 2.5% in high-income countries and projected increases due to population ageing. Randomized controlled trials in valvular heart disease have undergone substantial evolution, shifting from mortality-focused endpoints toward comprehensive assessments integrating imaging parameters and patient-centered outcomes. Cardiovascular imaging modalities, including echocardiography, cardiac computed tomography, and cardiac magnetic resonance, have become pivotal in trial design, patient selection, and endpoint definition. Recent landmark trials in aortic stenosis, including EARLY-TAVR and EVOLVED, have challenged traditional symptom-based intervention thresholds by incorporating imaging biomarkers of subclinical myocardial dysfunction and cardiac damage staging. In aortic regurgitation, the paucity of randomized controlled trials evidence contrasts with emerging transcatheter technologies, highlighting critical knowledge gaps. Mitral regurgitation trials have demonstrated the importance of patient phenotyping, with divergent outcomes between COAPT and MITRA-FR emphasizing the role of imaging in optimal patient selection. The recent TRILUMINATE and TRISCEND trials have transformed tricuspid regurgitation management through transcatheter interventions, prioritizing quality-of-life improvements alongside traditional clinical endpoints. Future directions include standardization of imaging protocols across modalities, development of artificial intelligence-enhanced analysis, and integration of multiparametric biomarkers for personalized risk stratification. The paradigm shift toward imaging-guided, patient-centered trial design represents a fundamental reimagining of therapeutic success in valvular heart disease, moving beyond procedural outcomes toward comprehensive assessment of clinical benefit and improved patient care.
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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 | 0.118 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".