Multisystem inflammatory syndrome in children (MIS-C) and COVID-19: a review of the literature
Bibliographic record
Abstract
Multisystem Inflammatory Syndrome in Children (MIS-C) is a novel pediatric hyperinflammatory syndrome that has recently emerged globally as a potential complication of COVID-19 infection and has features similar to Kawasaki Disease (KD). In this article, a review existing literature on MIS-C was conducted to identify trends in patient characteristics, clinical and biological features, treatment, and outcomes. MIS-C affects previously healthy school-age children, with over-representation of those of Black and Afro-Caribbean descent. It presents with fever, gastrointestinal complaints, and KD-type features including rash and conjunctivitis. Laboratory and imaging studies demonstrate evidence of systemic inflammation and myocarditis. Accordingly, children are often critically ill and require intensive care admission and organ support. However, prompt anti-inflammatory treatment with intravenous immunoglobulin, steroids, and aspirin appears to lead to favourable outcomes. Though evidence of current coronavirus infection by RT-PCR is variable, most children have positive serology results indicating prior infection, which supports theories of MIS-C as a dysfunctional post-infectious immune process. Though similar to KD in some ways, MIS-C has important differences in its patient characteristics, clinical features, and cardiac involvement. Large-scale case registries and analysis of resulting data will be crucial to refining our understanding of MIS-C to ensure optimal outcomes for children worldwide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".