Systemic Inflammation May Induce Cardiac Injury in COVID-19 Patients Including Children and Adolescents Without Underlying Cardiovascular Diseases: A Systematic Review
Why this work is in the frame
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Bibliographic record
Abstract
Coronavirus disease 2019(COVID-19) is an ongoing global pandemic with a daily increasing number of affected individuals and a relatively high mortality rate. COVID-19 patients that develop cardiac injury are at increased risk of a worse clinical course with higher rates of mortality. Increasing amounts of evidence suggest that a system-wide inflammatory response and a cytokine storm mediated type syndrome plays a crucial role in disease progression. This systematic review investigates the possible role of hyperinflammation in inducing cardiac injury as one of the severe complications of COVID-19. A systematic literature search was performed using PubMed, Embase and Scopus databases to identify relevant clinical studies that investigated cardiovascular injury manifestations and reported inflammatory and cardiac biomarkers in COVID-19 patients. Only 29 studies met our inclusion criteria and the majority of these studies demonstrated significantly elevated inflammatory and cardiac blood markers. It was evident that underlying cardiovascular diseases may increase the risk of developing cardiac injury. However, many COVID-19 patients included in this review, developed different types of cardiac injury without having any underlying cardiovascular diseases. Furthermore, many of these patients were either children or adolescents. Therefore, age and comorbidities may not always be the two main risk factors that dictate the severity and outcome of COVID-19. Further investigations are required to understand the underlying mechanisms of pathogenicity as an urgent requirement to develop the appropriate treatment and prevention strategies. These strategies may specifically target hyperinflammation as a suspected driving factor for some of the severe complications of COVID-19.
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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 | 0.012 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it