Eosinophilic myocarditis: two case reports and review of the literature
Bibliographic record
Abstract
BACKGROUND: Eosinophilic myocarditis is a rare and often under-diagnosed subtype of myocarditis with only around 30 cases published in the medical literature. In this article we present two patients with eosinophilic myocarditis with the aim to demonstrate the often elusive nature of the disease and present the current scientific literature on this topic. CASE PRESENTATION: A 76 years old Caucasian gentleman and a 36 years old Aboriginal gentleman both presenting with heart failure symptoms were eventually diagnosed with eosinophilic myocarditis after extensive evaluation. Their presentation, assessment, and medical management is explored in this article. CONCLUSIONS: Eosinophilic myocarditis remains a rare and likely under-diagnosed subtype of myocarditis. The key features of this disease include myocardial injury in the setting of non-contributory coronary artery disease. Endomyocardial biopsy remains the definitive gold standard for diagnosis of noninfectious eosinophilic myocarditis. Non-invasive cardiac imaging in the setting of peripheral eosinophilia can be strongly suggestive of eosinophilic myocarditis with potential for earlier diagnosis. Failure to diagnose eosinophilic myocarditis and the delay of therapy may lead to irreversible myocardial injury. Therapies for this disease have yet to be validated in large prospective studies.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".