A Systematic Review of COVID - 19 Induced Myocarditis - Symptomatology, Prognosis, and Clinical Findings
Bibliographic record
Abstract
Authors: Vikash Jaiswal, Shavy Nagpal, Christine Angela E.Labitag, Janelle Tayo, Abhinav Patel, Kevin Bryan Lo, Rupalakshmi Vijayan, Wanessa F Matos, Sadia Yaqoob, Priyanka Panday, Saloni Savani, Zeinab Alnahas, Arushee Bhatnagar, Yoandra Diaz, John R. Dylewski, Own Khraisat, Mohammed JM Ghanim. Objective: With the advent of a novel coronavirus in December 2019, several case studies have reported its adversity on cardiac cells. We conducted a systematic review that describes the symptomatology, prognosis, and clinical findings of patients with COVID-19-related myocarditis. Methods: Search engines including PubMed, Google Scholar, Cochrane Central, and Web of Science were queried for "SARS-CoV-2" or "COVID 19" and "myocarditis." PRISMA guidelines were employed, and peerreviewed journals in English related to COVID- 19 were included. Results: This systematic review included 22 studies and 37 patients. Eight patients (36%) were confirmed myocarditis, while the rest were possible myocarditis. Most patients had elevated cardiac biomarkers, including troponin, CRP, CK, CK-MB, and NT-pro BNP. Electrocardiogram results noted tachycardia (47%), left ventricular hypertrophy (50%), ST-segment alterations (41%), and T wave inversion (18%). Echocardiography presented reduced LVEF (77%), left ventricle abnormalities (34%), right ventricle aberrations (12%), and pericardial effusion (71%). Further, CMR showed reduced myocardial edema (75%), non-ischemic patterns (50%), and hypokinesis (26%). The mortality was significant at 25%. Conclusions: Mortality associated with COVID-19 myocarditis appears significant but underestimated. Further studies are warranted to evaluate and quantify patients’ actual prognosis and outcomes with COVID-19 myocarditis.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".