Severity of COVID-19 in Hospitalized Immunocompromised Children Across Canada
Bibliographic record
Abstract
BACKGROUND: The association of immunocompromised states with pediatric COVID-19 outcomes remains unclear. This study assessed COVID-19 severity in hospitalized children with and without immunocompromising conditions. METHODS: Children <17 years hospitalized in Canada for COVID-19 (April 2020-December 2022) were identified through the Canadian Paediatric Surveillance Program and Canadian Immunization Monitoring Program, ACTive. Immunocompromised children (IC) were those with immune-compromising conditions and/or on immunosuppressive treatment. Severe COVID-19 was defined as intensive care unit admission, ventilator/hemodynamic support, organ complications or death. Adjusted risk ratios (aRR) for severe COVID-19 among IC versus nonimmunocompromised children (non-IC) were calculated using Poisson regression, adjusting for age, sex, other underlying conditions, SARS-CoV-2 lineage and vaccination. RESULTS: Among 3218 children hospitalized for COVID-19, 354 (11.0%) were IC. IC were older [median age 6.7 years (interquartile range = 3.6-11.8)] than non-IC [1.2 years (interquartile range = 0.2-4.8); P < 0.001]. IC experienced less respiratory distress than non-IC (20.9% vs. 48.1%). Severe COVID-19 (14.1% vs. 29.0%; P < 0.001), respiratory support (16.1% vs. 35.6%; P < 0.001) and intensive care unit admission (5.9% vs. 17.4%; P < 0.001) were less frequent in IC. IC were less likely to have severe COVID-19 than non-IC [aRR = 0.46 (95% confidence interval [CI]: 0.32-0.65)], with both immunodeficiency [aRR = 0.53 (95% CI: 0.39-0.73)] and immunosuppression [aRR = 0.40 (95% CI: 0.23-0.73)] subcategories independently associated with reduced risk. Compared to non-IC with other conditions, IC had a lower risk of severe COVID-19 [aRR = 0.35 (95% CI: 0.25-0.47)]. CONCLUSIONS: Hospitalized IC exhibited a lower risk of severe COVID-19 than non-IC, potentially reflecting lower admission thresholds for IC with respiratory infections.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".