Cardiac MRI Assessment of Nonischemic Myocardial Inflammation: State of the Art Review and Update on Myocarditis Associated with COVID-19 Vaccination
Bibliographic record
Abstract
Myocarditis is a nonischemic inflammatory disease of the myocardium that can be triggered by a multitude of events, including viral infection and toxins. Recently, there has been heightened interest in myocarditis given its association with COVID-19 vaccination. Timely identification of myocarditis can affect patient management and prognosis. Therefore, it is crucial for radiologists and cardiac imagers to understand the role of cardiac imaging to establish a diagnosis and inform treatment decisions. Cardiac MRI is the most important noninvasive imaging modality for evaluation of myocarditis, with typical findings of focal or diffuse myocardial edema and myocardial damage, including presence of late gadolinium enhancement. There are currently limited data available to indicate that the pattern of myocardial injury following COVID-19 vaccination is similar to other causes of myocarditis, although the severity of disease may be relatively mild. A description of the role of imaging and typical imaging features will be reviewed here, with a focus on emerging data in the setting of myocarditis after COVID-19 vaccination. Keywords: MRI, Heart, Inflammation © RSNA, 2021
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".