Multisystemic Inflammatory Syndrome in Neonates: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Multisystem inflammatory syndrome in neonates (MIS-N) related to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has increasingly been reported worldwide amid the spread of the SARS-CoV-2 pandemic. METHODS: We searched PubMed, EMBASE, and CINAHL and preprint servers (BioRxiv.org and MedRxiv.org) using a specified strategy integrating Medical Subject Headings terms and keywords until October 20, 2021. Our aim was to systematically review demographic profiles, clinical features, laboratory parameters, complications, treatments, and outcomes of neonates with MIS-N. Studies were selected when fulfilling the inclusion criteria. Articles were included if they fulfilled the World Health Organization (WHO), Centers for Disease Control (CDC) definitions of MIS-C, or our proposed definition. RESULTS: Sixteen reports of MIS-N including 47 neonates meeting MIS-N criteria were identified. Presentation included cardiovascular compromise (77%), respiratory involvement (55%), and fever in (36%). Eighty-three percent of patients received steroids, and 76% received immunoglobulin. Respiratory support was provided to 60% of patients and inotropes to 45% of patients. Five (11%) neonates died. CONCLUSION: The common presentation of MIS-N included cardiorespiratory compromise with the possibility of high mortality. Neonates with MIS-N related to SARS-CoV-2 may be at higher risk of adverse outcomes.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".