Review of MIS-C Clinical Protocols and Diagnostic Pathways: Towards a Consensus Algorithm
Bibliographic record
Abstract
Background: The emergence of multisystem inflammatory syndrome in children (MIS-C) during the severe acute respiratory syndrome coronavirus 2 pandemic led to the development of institutional clinical pathways based on expert opinion. We assessed North American paediatric centres' adaptation to MIS-C and analysed the degree of agreement between algorithms on tiered clinical investigations. Methods: This study evaluated MIS-C diagnostic algorithms from 50 tertiary centres developed between May 2020 and December 2021 in the United States and Canada obtained online and through colleagues in various institutions. Descriptive statistics were used to analyse results. Results: All clinical pathways used a tiered approach, and most required coronavirus disease 2019 polymerase chain reaction testing on presentation. Over one-quarter used a 24-hour fever to initiate investigations, and another quarter used 3 days. Basic biochemical workup was performed in all centres on presentation (complete blood count, inflammatory markers, hepatic, and renal functions). Specialized investigation was generally reserved for secondary testing (cardiac biomarkers, electrocardiogram and echo, and coagulation panel). Institutions were divided on several investigations for tier distribution, including urine studies, blood cultures, chest radiograph, and severe acute respiratory syndrome coronavirus 2 serology. Subspecialty consultations were reserved for second-line testing, including cardiology, infectious disease, and rheumatology. Finally, we propose a composite algorithm representative of the consulted pathways. Conclusions: Faced with an unprecedented clinical challenge, paediatric institutions responded swiftly with evaluation standardization, adapting to evolving knowledge. Most pathways agreed on initial basic screening tests followed by secondary workup including cardiac investigations. These protocols, developed during a high level of uncertainty, require comparative assessment on efficacy and superiority.
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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 | 0.173 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".