COVID-19 and Myocarditis: What Do We Know So Far?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it