Immunomodulatory Therapy in Children with Multisystem Inflammatory Syndrome (PIMS, MIS-C): A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background Multisystem Inflammatory Syndrome in Children (MIS-C) is a severe systemic hyperinflammatory condition leading to multiorgan dysfunction in a small number of children with COVID-19. Immunomodulatory therapy, including Intravenous Immunoglobulin (IVIG) and corticosteroids, is the mainstay of treatment. This meta-analysis was conducted to evaluate the effectiveness of three treatment strategies (IVIG and steroids, IVIG-only, and steroids-only) in the management of MIS-C Methods A systematic search of databases and grey literature sources was conducted to identify relevant RCTs and observational studies. RevMan 5.4 was used to analyze all the outcomes with risk ratio (RR) as the effect measure. The Newcastle-Ottawa scale was used to assess the risk of bias in observational studies. Cochrane’s revised “Risk of Bias” tool (RoB 2.0) was used to assess bias in RCTs. Results We included six cohort studies and one RCT in our meta-analysis. Compared to the IVIG-only group, the combination of IVIG and steroids reduced the treatment failure/persistence of fever [RR 0.70; 95% CI: 0.57-0.87], cardiovascular dysfunction [RR 0.62; 95% CI: 0.40-0.96], and the need for adjunctive immunomodulatory therapy [RR 0.49; 95% CI: 0.40-0.59]. The combination therapy and IVIG-only therapy had similar rates of left ventricular dysfunction, the need for inotropes, and the need for ventilatory support. According to our analysis, the steroids-only therapy and IVIG-only therapy had similar clinical outcomes. Conclusion Our meta-analysis shows that combination therapy with IVIG and steroids is more effective than IVIG or steroids alone in reducing treatment failure, cardiovascular dysfunction, and the need for additional immunomodulatory drugs in MIS-C. However, these findings must be interpreted cautiously due to the predominance of observational data, heterogeneity among studies, and the limited number of studies included. More large-scale RCTs are needed to support or refute our findings.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".