What are the scientific facts about the symptoms and treatment of COVID-19 in the pediatric population? A systematic review with overview
Bibliographic record
Abstract
Considering the lower prevalence of pediatric patients with COVID-19, health professionals are more familiar with interventions aimed at adults; thus, understanding the symptoms and which approaches can be useful for pediatric patients with COVID-19 is of great importance for health professionals. The study aimed to aggregate scientific data on procedures and treatments performed on pediatric patients (aged 6 to 17 years) with COVID-19. A systematic literature search was performed in five electronic databases (i.e., PubMed, CINAHL, Google Scholar, and LILACS). Literature analyses were performed in English and Chinese, between November 2019 and December 2020. For data classification, the web application Rayyan® was used. Studies have shown that in most cases, children are asymptomatic. However, when symptomatic, they present fever, cough, intestinal infection, and vomiting. It is noteworthy that the respiratory rate and stool tests were significant indicators of the disease. Regarding treatment, oxygen support is essential during the hospitalization of patients and, antiviral therapy with Lopinavir and Ritonavir has shown significant results in a few isolated cases. It is concluded that the main symptoms of SARS-CoV-2 in pediatric patients are mild symptoms similar to those of common flu. In addition, respiratory rate and examinations based on fecal samples are good indicators of the disease in children of both sexes, as well as antiviral therapies and early isolation at the beginning of the disease are significant for the healing process.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".