Multisystem inflammatory syndrome (MIS-C): a systematic review and meta-analysis of clinical characteristics, treatment, and outcomes
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: The clinical cases of patients with multisystem inflammatory syndrome (MIS-C) were analyzed via a systematic review and meta-analysis of the clinical findings, treatments, and possible outcomes of articles retrieved via database searches. SOURCES: The authors searched the PubMed, Scielo, Web of Science, Science Direct, EMBASA, EBSCO, and Scopus databases for articles containing the keywords "multisystem inflammatory syndrome in children" or "MIS-C" or "PIMS-TS" or "SIMP" and "COVID-19" or "SARS-CoV-2" published between December 1st, 2019 and July 10th, 2021. Patient characteristics, tissue and organ comorbidities, the incidence of symptoms after COVID-19 infection, treatment, and patient evolution in the articles found were evaluated. The data were abstracted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Newcastle-Ottawa Scale (NOS). FINDINGS: In total, 98 articles (2275 patients) were selected for demographics, clinical treatment, and outcomes of patients diagnosed with MIS-C. The average age of children with MIS-C, 56.8% of whom were male, was of nine years. Fever (100%), gastrointestinal (GI) (82%), and abdominal pain (68%) were the decisive symptoms for the diagnosis of MIS-C. Shock and/or hypotension were common in patients with MIS-C. Cardiac symptoms (66%) predominated over respiratory (39%) and neurological (28%) symptoms. MIS-C treatment followed the common guidelines for treating children with septic shock and Kawasaki disease (KD) and proved to be effective. CONCLUSIONS: This meta-analysis highlights the main clinical symptoms used for the diagnosis of MIS-C, the differences between MIS-C and KD, and the severity of the inflammatory process and urgency for hospital care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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