COVID-19 and Myocarditis: What Do We Know So Far?
Bibliographic record
Abstract
COVID-19 has been declared a global pandemic by the World Health Organization and is responsible for hundreds of thousands of deaths worldwide. COVID-19 is caused by SARS-CoV-2, and common clinical symptoms include fever, cough, sore throat, headache, and fatigue. Myocardial injury is relatively common in patients with COVID-19, accounting for 7%-23% of cases, and is associated with a higher rate of morbidity and mortality. There is a discrepancy in the literature about myocarditis as the etiology of myocardial injury in patients with COVID-19; although many anecdotal reports of myocarditis have been noted, there are only a handful of case reports in the literature about myocarditis related to COVID-19. In this review we summarize the most up to date literature around the association between COVID-19 and myocarditis and provide clinicians a practical framework about the clinical manifestations, diagnostic tools, and treatment options currently available. Importantly, this review will heighten suspicion for myocarditis as an etiology of myocardial injury in COVID-19 patients, therefore improving clinical outcomes and encouraging shared clinical decision-making. This will also open the door for further research to build around this review. Emergent treatment options for COVID-19 are in clinical trials and might be of benefit to COVID-19 patients with myocarditis in addition to current guideline-based recommendations.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".