Review of clinical characteristics and laboratory findings of COVID-19 in children-Systematic review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review and meta-analysis to assess the prevalence of various clinical symptoms and laboratory findings of COVID-19 in children. METHODS: PubMed, MEDLINE, and SCOPUS databases were searched to include studies conducted between January 1, 2020, and July 15, 2020 which reported data about clinical characteristics and laboratory findings in laboratory-confirmed diagnosis of COVID-19 in pediatric patients. Random effects meta-analysis using generalized linear mixed models was used to estimate the pooled prevalence. RESULTS: The most prevalent symptom of COVID-19 in children was 46.17% (95%CI 39.18-53.33%), followed by cough (40.15%, 95%CI 34.56-46.02%). Less common symptoms were found to be dyspnea, vomiting, nasal congestion/rhinorrhea, diarrhea, sore throat/pharyngeal congestion, headache, and fatigue. The prevalence of asymptomatic children was 17.19% (95%CI 11.02-25.82%). The most prevalent laboratory findings in COVID-19 children were elevated Creatinine Kinase (26.86%, 95%CI 16.15-41.19%) and neutropenia (25.76%, 95%CI 13.96-42.58%). These were followed by elevated LDH, thrombocytosis, lymphocytosis, neutrophilia, elevated D Dimer, Elevated CRP, elevated ESR, leukocytosis, elevated AST and leukopenia. There was a low prevalence of elevated ALT and lymphopenia in children with COVID- 19. CONCLUSIONS AND RELEVANCE: This study provides estimates of the pooled prevalence of various symptoms and laboratory findings of COVID-19 in the pediatric population.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".