CT and MR Imaging Findings in Patients with Acquired Heart Disease at Risk for Sudden Cardiac Death
Bibliographic record
Abstract
Noninvasive imaging is an important screening and diagnostic tool in conditions associated with sudden cardiac death. The most common cause of sudden cardiac death is coronary artery disease, with myocarditis, cardiac sarcoidosis, and dilated and infiltrative cardiomyopathies being less common acquired causes. Common risk factors for sudden cardiac death, regardless of the disease process, include severe ventricular dysfunction and the presence of macroscopic scar seen at delayed contrast material-enhanced imaging. Recent advances in electrocardiographically (ECG) gated cardiac magnetic resonance (MR) imaging and multidetector computed tomography (CT) have led to increased referrals for cross-sectional imaging; thus, cardiac radiologists should be familiar with the disease entities associated with sudden cardiac death. Inflammatory processes and cardiomyopathies are best depicted with cardiac MR imaging. Steady-state free precession cine sequences coupled with inversion-recovery prepared gradient-echo T1-weighted sequences performed after the intravenous administration of gadolinium-based contrast material should form the basis of cardiac MR imaging protocols for cardiomyopathy. A clinical history that is suggestive of myocardial ischemia, specific requests to exclude coronary artery disease, or contraindications for MR imaging may imply that multidetector CT would be more appropriate. Nevertheless, both cardiac MR imaging and ECG-gated multidetector CT offer robust diagnosis and risk stratification for individual disease processes associated with sudden cardiac death.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".