Management of Multisystem Inflammatory Syndrome in Children Associated With COVID-19: A Survey From the International Kawasaki Disease Registry
Bibliographic record
Abstract
BACKGROUND: Since April 2020, there have been numerous reports of children presenting with systemic inflammation, often in critical condition, and with evidence of recent infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This condition, since defined as the multisystem inflammatory syndrome in children (MIS-C), is assumed to be a delayed immune response to coronavirus disease 2019 (COVID-19), and there are frequently cardiac manifestations of ventricular dysfunction and/or coronary artery dilation. METHODS: We surveyed the inpatient MIS-C management approaches of the members of the International Kawasaki Disease Registry across 38 institutions and 11 countries. RESULTS: Among the respondents, 56% reported using immunomodulatory treatment for all MIS-C patients, regardless of presentation. Every respondent reported use of intravenous immunoglobulin (IVIG), including 53% administering IVIG in all patients. Steroids were most often used for patients with severe clinical presentation or lack of response to IVIG, and only a minority used steroids in all patients (14%). Acetylsalicylic acid was frequently used among respondents (91%), including anti-inflammatory and/or antiplatelet dosing. Respondents reported use of prophylactic anticoagulation, especially in patients at higher risk for venous thromboembolism, and therapeutic anticoagulation, particularly for patients with giant coronary artery aneurysms. CONCLUSIONS: There is variation in management of MIS-C patients, with suboptimal evidence to assess superiority of the various treatments; evidence-based gaps in knowledge should be addressed through worldwide collaboration to optimize treatment strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".