Multimodality Imaging to Understand Mechanisms of Right Ventricular Disease
Bibliographic record
Abstract
Right ventricular (RV) disease is commonly encountered in patients with cardiovascular conditions and is associated with adverse prognosis. The principal pathogenic mechanisms giving rise to RV pathology include RV pressure overload, RV volume overload, and RV myocardial diseases. Noninvasive cardiac imaging is commonly used to detect the conditions associated with RV disease and ultimately guide therapeutic decisions. Transthoracic echocardiogram is usually the first-line test in patients with suspected RV disease and it provides relevant information on biventricular size and function, valvular abnormalities, and cardiac hemodynamics including pulmonary pressures. Cardiac magnetic resonance imaging is considered the reference standard noninvasive imaging test for quantifying ventricular size and function and cardiac shunts and has a secondary role for assessing valvular disease when echocardiography is nondiagnostic. Cardiac magnetic resonance imaging also provides insight into RV myocardial diseases such as inflammation, infarction, and infiltration. Nuclear cardiology and cardiac computed tomography imaging can also be used to inform on specific RV disease mechanisms originating from lung disease and pulmonary vasculature disorders. In this review, we discuss the role and utility of cardiac imaging in characterizing RV mechanisms of disease and provide a suggested framework for clinicians to appropriately utilize imaging in these clinical scenarios.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".