Multicenter cohort study of multisystem inflammatory syndrome in children (MIS-C)
Bibliographic record
Abstract
Abstract BACKGROUND SARS-CoV-2 infection can lead to multisystem inflammatory syndrome in children (MIS-C). We investigated risk factors for severe disease and explored changes in severity over time. METHODS Children up to 17 years of age admitted March 1, 2020 through March 7 th , 2021 to 15 hospitals in Canada, Iran and Costa Rica with confirmed or probable MIS-C were included. Descriptive analysis and comparison by diagnostic criteria, country, and admission date was performed. Adjusted absolute average risks (AR) and risk differences (RD) were estimated for characteristics associated with ICU admission or cardiac involvement. RESULTS Of 232 cases (106 confirmed) with median age 5.8 years, 56% were male, and 22% had comorbidities. ICU admission occurred in 73 (31%) but none died. Median length of stay was 6 days (inter-quartile range 4-9). Children 6 to 12 years old had the highest AR for ICU admission (44%; 95% confidence interval [CI] 34-53). Initial ferritin greater than 500 mcg/L was associated with ICU admission. When comparing cases admitted up to October 31, 2020 to those admitted later, the AR for ICU admission increased from 25% (CI 17-33) to 37% (CI 29-46) and for cardiac involvement from 44% (CI 35-53) to 75% (CI 66-84). Risk estimates for ICU admission in the Canadian cohort demonstrated a higher risk in December 2020-March 2021 compared to March-May 2020 (RD 25%; 95%CI 7-44). INTERPRETATION MIS-C occurred primarily in previously well children. Illness severity appeared to increase over time. Despite a high ICU admission incidence, most children were discharged within one week.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".