Resource use and disease severity of children hospitalized for COVID-19 versus multisystem inflammatory syndrome in children (MIS-C) in Canada
Bibliographic record
Abstract
Background: Direct comparisons of paediatric hospitalizations for acute coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) can inform health system planning. We describe the absolute and relative hospital burden of acute paediatric COVID-19 and MIS-C in Canada. Methods: This national prospective study was conducted via the Canadian Paediatric Surveillance Program from March 2020-May 2021. Children younger than 18 years old and hospitalized for acute COVID-19 or MIS-C were included in the analysis. Outcomes included supplemental oxygen (low-flow oxygen or high-flow nasal cannula), ventilation (non-invasive or conventional mechanical), vasopressors, paediatric intensive care unit (PICU) admission, or death. Adjusted risk differences (aRD) and 95% confidence intervals (CI) were calculated to identify factors associated with each diagnosis. Results: Overall, we identified 330 children hospitalized for acute COVID-19 (including five deaths) and 208 hospitalized for MIS-C (including zero deaths); PICU admission was required for 49.5% of MIS-C hospitalizations versus 18.2% of acute COVID-19 hospitalizations (aRD 20.3; 95% CI, 9.9-30.8). Resource use differed by age, with children younger than one year hospitalized more often for acute COVID-19 (aRD 43.4% versus MIS-C; 95% CI, 37.7-49.1) and more children 5-11 years hospitalized for MIS-C (aRD 38.9% vs. acute COVID-19; 95% CI, 31.0-46.9). Conclusion: While there were more hospitalizations and deaths from acute paediatric COVID-19, MIS-C cases were more severe, requiring more intensive care and vasopressor support. Our findings suggest that both acute COVID-19 and MIS-C should be considered when assessing the overall burden of severe acute respiratory syndrome coronavirus 2 in hospitalized children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".