The unique role of cardiovascular magnetic resonance imaging in acute myocarditis
Bibliographic record
Abstract
This article addresses the specific diagnostic information provided by cardiovascular magnetic resonance (CMR) in patients with suspected acute myocarditis. It gives an overview of the current evidence of the ability of CMR to detect myocardial inflammation and discusses the added value as well as its limitations in clinical settings. Because of the large variety of symptoms and the limited specificity of other non-invasive procedures, the identification of myocardial inflammation is of paramount importance. Because of its accuracy in imaging ventricular volumes and function and its unique ability to visualize myocardial edema, scar, and other tissue abnormalities, CMR has emerged as the prime non-invasive diagnostic tool in patients with acute myocarditis. The presence of myocardial inflammation is not specific to viral myocarditis or other forms of acute myocardial injury, and the regional distribution within the myocardium helps differentiate acute myocarditis from other diseases. The currently recommended diagnostic criteria (Lake Louise Criteria) include markers for hyperemia/capillary leak, edema, and inflammatory scarring. Their diagnostic accuracy of close to 80% is satisfactory to rule in myocarditis, yet the negative predictive value is less than 70%. Novel CMR techniques, especially T1 and T2 mapping, have been shown to further improve the diagnostic utility.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".