Characteristics of children admitted to hospital with acute SARS-CoV-2 infection in Canada in 2020
Bibliographic record
Abstract
BACKGROUND: Risk factors for severe outcomes of SARS-CoV-2 infection are not well established in children. We sought to describe pediatric hospital admissions associated with SARS-CoV-2 infection in Canada and identify risk factors for more severe disease. METHODS: We conducted a national prospective study using the infrastructure of the Canadian Paediatric Surveillance Program (CPSP). Cases involving children who were admitted to hospital with microbiologically confirmed SARS-CoV-2 infection were reported from Apr. 8 to Dec. 31 2020, through weekly online questionnaires distributed to the CPSP network of more than 2800 pediatricians. We categorized hospital admissions as related to COVID-19, incidental, or for social or infection control reasons and determined risk factors for disease severity in hospital. RESULTS: Among 264 hospital admissions involving children with SARS-CoV-2 infection during the 9-month study period, 150 (56.8%) admissions were related to COVID-19 and 100 (37.9%) were incidental infections (admissions for other reasons and found to be positive for SARS-CoV-2 on screening). Infants (37.3%) and adolescents (29.6%) represented most cases. Among hospital admissions related to COVID-19, 52 (34.7%) had critical disease, 42 (28.0%) of whom required any form of respiratory or hemodynamic support, and 59 (39.3%) had at least 1 underlying comorbidity. Children with obesity, chronic neurologic conditions or chronic lung disease other than asthma were more likely to have severe or critical COVID-19. INTERPRETATION: Among children who were admitted to hospital with SARS-CoV-2 infection in Canada during the early COVID-19 pandemic period, incidental SARS-CoV-2 infection was common. In children admitted with acute COVID-19, obesity and neurologic and respiratory comorbidities were associated with more severe disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".