COVID-19 mRNA Vaccination-Induced Myopericarditis in an Otherwise Healthy Young Male: An Evidence-Based Approach to Differentiating From Perimyocarditis
Bibliographic record
Abstract
A 29-year-old male, otherwise healthy with no past medical history, presented to the hospital after a two-day history of pleuritic chest pain with a fever. He had received his first dose of the mRNA-1273 coronavirus disease (COVID-19) vaccine (Moderna) two months prior without any adverse reactions. He received his second dose approximately 24 hours before symptom onset and hospital presentation. Work-up was unremarkable for respiratory, autoimmune, and rheumatological etiologies. The patient was found to have electrocardiogram features and symptoms in keeping with pericarditis, C-reactive protein elevation, and a peak high-sensitivity troponin level of 9,992 ng/L suggestive of a component of myocarditis. A dilemma arose regarding whether this patient should be diagnosed with perimyocarditis or myopericarditis, terms often used interchangeably without proper reference to the primary pathology, which can ultimately affect management. A subsequent echocardiogram was unremarkable, with a normal left ventricular systolic function, but cardiac resonance imaging revealed myocardial edema suggestive of myocarditis. Without convincing evidence for an alternative explanation after an extensive work-up of ischemic, autoimmune, rheumatological, and infectious etiologies, this patient was diagnosed with COVID-19 mRNA vaccine-induced myopericarditis. The patient fully recovered after receiving a treatment course of ibuprofen and colchicine. This case explores how the diagnosis of COVID-19 vaccine-induced myopericarditis was made and treated using an evidence-based approach, highlighting its differentiation from perimyocarditis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".