Prognostic factors for multisystem inflammatory syndrome in children: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Multisystem inflammatory syndrome in children (MIS-C) is a novel condition that can occur post-SARS-CoV-2 infection in children and adolescents. There is a paucity of evidence on the prognostic factors associated with MIS-C. The aim of this systematic review and meta-analysis was to summarise the prognostic factors for MIS-C development. METHODS: Five databases were systematically searched from January 2020 to May 2023 for studies reporting on prognostic factors for MIS-C using multivariable regression models. Random-effects meta-analyses were conducted to pool odds ratios for each prognostic factor. Risk of bias was rated using QUIPS and the GRADE framework was used to assess the certainty of evidence for each unique factor. RESULTS: Twelve observational studies (N = 18 024) were included, and 13 unique prognostic factors were amenable to meta-analysis. With moderate certainty, age <12 years, male sex and Black race probably increase the risk of MIS-C. Malignancy and underlying respiratory disease probably decrease the risk of MIS-C. Low-certainty evidence suggests that Asian race may increase the risk of MIS-C, and comorbidity may decrease the risk of MIS-C. CONCLUSION: Current literature presents several prognostic factors related to MIS-C following SARS-CoV-2 infection. Further research is necessary to elucidate the pathophysiologic mechanisms related to MIS-C.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".