Management of Multisystem Inflammatory Syndrome in Children: Decision-Making Regarding a New Condition in the Absence of Clinical Trial Data
Bibliographic record
Abstract
Multisystem inflammatory syndrome in children (MIS-C) is a new illness that evolved during the COVID-19 pandemic with initial reports of severe disease including use of extracorporeal membrane oxygenation and death. Institutions rapidly assembled task forces to develop treatment algorithms. At the national/international levels, collaboratives and associations assembled consensus writing groups to draft guidelines. These guidelines and algorithms were initially on the basis of expert opinion and small case series. Some groups used the Delphi approach, and the resultant guidelines often mimicked those for other conditions that resembled MIS-C, like Kawasaki disease (KD). For instance, intravenous immunoglobulin (IVIG), a known effective treatment for KD, was recommended for MIS-C. Early in the pandemic many favoured IVIG over steroids as first-line therapy. As evidence evolved so did some guidelines, which now endorse the dual use of IVIG with steroids as first-line therapy. In contrast, withholding immunotherapy became an option for some MIS-C patients with mild symptoms. Herein, we review guidelines and discuss the evidence informing early recommendations, how this has evolved, the role and limitations of expert opinion and observational data, and the importance of leveraging existing research infrastructures, such as the intensive care unit collaborative (Overcoming COVID-19 surveillance registry), and the International Kawasaki Disease Registry. Finally, we discuss strategies to rapidly develop, deploy, and adapt clinical trials evaluating the treatment of such rare conditions in children, which might include alternatives to conventional clinical trial design. The emergence of MIS-C during the COVID-19 pandemic has highlighted unmet needs regarding research of a new condition.
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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 | 0.052 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".