Multimodality imaging in valvular heart disease: how to use state-of-the-art technology in daily practice
Bibliographic record
Abstract
Our understanding of the complexities of valvular heart disease (VHD) has evolved in recent years, primarily because of the increased use of multimodality imaging (MMI). Whilst echocardiography remains the primary imaging technique, the contemporary evaluation of patients with VHD requires comprehensive analysis of the mechanism of valvular dysfunction, accurate quantification of severity, and active exclusion extravalvular consequences. Furthermore, advances in surgical and percutaneous therapies have driven the need for meticulous multimodality imaging to aid in patient and procedural selection. Fundamental decision-making regarding whom, when, and how to treat patients with VHD has become more complex. There has been rapid technological advancement in MMI; many techniques are now available in routine clinical practice, and their integration into has the potential to truly individualize management strategies. This review provides an overview of the current evidence for the use of MMI in VHD, and how various techniques within each modality can be used practically to answer clinical conundrums.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".