Severe COVID-19 Infection and Pediatric Comorbidities: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: There is limited information on the severity of COVID-19 infection in children with comorbidities. We investigated the effects of pediatric comorbidities on COVID-19 severity by means of a systematic review and meta-analysis of published literature. Methods: PubMed, Embase, and Medline databases were searched for publications on pediatric COVID-19 infections published January 1 st to October 5 th , 2020. Articles describing at least one child with and without comorbidities, COVID-19 infection, and reported outcomes were included. Results: 42 studies containing 275,661 children without comorbidities and 9,353 children with comorbidities were included. Severe COVID-19 was present in 5.1% of children with comorbidities, and in 0.2% without comorbidities. Random-effects analysis revealed a higher risk of severe COVID-19 among children with comorbidities than for healthy children; relative risk ratio 1.79 (95% CI 1.27 -2.51; I 2 = 94%). Children with underlying conditions also had a higher risk of COVID-19-associated mortality; relative risk ratio 2.81 (95% CI 1.31 -6.02; I 2 = 82%). Children with obesity had a relative risk ratio of 2.87 (95% CI 1.16 -7.07; I 2 = 36%). Conclusions: Children with comorbidities have a higher risk of severe COVID-19 and associated mortality than children without underlying disease. Additional studies are required to further evaluate this relationship.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".