The spectrum of multisystem inflammatory syndrome (MIS-C) in children infected with severe acute respiratory syndrome coronavirus 2
Bibliographic record
Abstract
Introduction: The impact of SARS-CoV-2 infections in children has generally been described as relatively benign. However, since April 2020, there have been reports of a new multisystem inflammatory illness affecting children and related to COVID-19 termed multisystem inflammatory syndrome in children (MIS-C). Aim: To describe 3 cases of children diagnosed with MIS-C and discuss the disease spectrum. Methods: We collected and reviewed data from 3 cases diagnosed with MIS-C admitted to our pediatric ward between October 2020 and January 2021. Discussion: MIS-C is a newly described disease that spans a spectrum of phenotypes and severity, and while it shares clinical similarities with Kawasaki disease, it has a unique set of epidemiological, laboratory, and prognostic characteristics. In this review, we hope to add to the understanding of this new entity. Statement of Novelty: This report discusses 3 cases of MIS-C and elaborates on the spectrum and immunology of this entity. Our cases are unique in their relatively wide spectrum and variability. We hope our own experience with MIS-C adds to the accumulating knowledge and understanding of this emerging disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".