Screening Multisystem Inflammatory Syndrome in Children: Accuracy of the American College of Rheumatology Screening Algorithm
Bibliographic record
Abstract
OBJECTIVE: Diagnosing multisystem inflammatory syndrome in children (MIS-C) is challenging, as it shares clinical features with other childhood febrile illnesses. In response to the emergence of this syndrome during the pandemic, the American College of Rheumatology (ACR) developed a screening algorithm for the evaluation of MIS-C. We aimed to determine the accuracy of the ACR algorithm in distinguishing patients with MIS-C from other febrile children. METHODS: A single-center case-control study was conducted on children with suspected or confirmed MIS-C from March 2020 to March 2022. The cohort was divided into 2 groups: the MIS-C group, including children with confirmed MIS-C, and febrile controls, consisting of children suspected but ultimately not diagnosed with MIS-C. The ACR MIS-C screening algorithm was retrospectively applied to both groups. The diagnosis obtained was compared with the World Health Organization (WHO) and Council of State and Territorial Epidemiologists/US Centers for Disease Control (CSTE/CDC) case definitions. Sensitivity, specificity, and 95% CIs were calculated. RESULTS: There were 402 children (241 MIS-C, 161 febrile controls) included. Median age was 4.2 years, and 58.9% were male. The ACR screening algorithm had 74.3% sensitivity, 99.2% specificity, and 86.7% balanced accuracy when the WHO case definition was used as the gold standard; and 86.2% sensitivity, 95.8% specificity, and 91% balanced accuracy when the CSTE/CDC case definition was the gold standard. CONCLUSION: The ACR MIS-C screening algorithm demonstrates high specificity, high accuracy, and good sensitivity in identifying children with MIS-C at disease onset. Despite being developed early in the pandemic with limited data available, the ACR algorithm effectively differentiates children with MIS-C from febrile controls.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".