Multisystem inflammatory syndrome in children (MIS-C) and neonates (MIS-N) associated with COVID-19: optimizing definition and management
Bibliographic record
Abstract
During the SARS-CoV-2-associated infection (COVID-19), pandemic initial reports suggested relative sparing of children inversely related to their age. Children and neonates have a decreased incidence of SARS-CoV-2 infection, and if infected they manifested a less severe phenotype, in part due to enhanced innate immune response. However, a multisystem inflammatory syndrome in children (MIS-C) or paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 emerged involving coronary artery aneurysms, cardiac dysfunction, and multiorgan inflammatory manifestations. MIS-C has many similarities to Kawasaki disease and other inflammatory conditions and may fit within a spectrum of inflammatory conditions based on immunological results. More recently neonates born to mothers with SARS-CoV-2 infection during pregnancy demonstrated evidence of a multisystem inflammatory syndrome with raised inflammatory markers and multiorgan, especially cardiac dysfunction that has been described as multisystem inflammatory syndrome in neonates (MIS-N). However, there is a variation in definitions and management algorithms for MIS-C and MIS-N. Further understanding of baseline immunological responses to allow stratification of patient groups and accurate diagnosis will aid prognostication, and inform optimal immunomodulatory therapies. IMPACT: Multisystem inflammatory system in children and neonates (MIS-C and MIS-N) post COVID require an internationally recognized consensus definition and international datasets to improve management and plan future clinical trials. This review incorporates the latest review of pathophysiology, clinical information, and management of MIS-C and MIS-N. Further understanding of the pathophysiology of MIS-C and MIS-N will allow future targeted therapies to prevent and limit clinical sequelae.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".