Clinical characteristics of children with COVID-19: a rapid review and meta-analysis

  1. on behalf of COVID-19 Evidence and Recommendations Working Group
  2. Zijun Wang
  3. Qi Zhou
  4. Chenglin Wang
  5. Qianling Shi
  6. Shuya Lu
  7. Yanfang Ma
  8. Xufei Luo
  9. Yangqin Xun
  10. Weiguo Li
  11. Muna Baskota
  12. Yinmei Yang
  13. Hui Zhai
  14. Toshio Fukuoka
  15. Hyeong Sik Ahn
  16. Myeong Soo Lee
  17. Zhengxiu Luo
  18. Enmei Liu
  19. Yaolong Chen

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.04.13.20064352: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationFor randomized controlled trials (RCTs), we will assess the risk of bias independently using Cochrane risk-of-bias tool (17).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: We comprehensively searched the following electronic databases: Cochrane library, MEDLINE (via PubMed), Embase, Web of Science, China Biology Medicine disc (CBM), China National Knowledge Infrastructure (CNKI), and Wanfang Data from their inception until March 31, 2020 with the terms “2019-novel coronavirus”, “SARS-CoV-2”, “COVID-19”, “2019-nCoV”, “clinical features” and their derivatives.
    Cochrane library
    suggested: (Cochrane Library, RRID:SCR_013000)
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    We also searched World Health Organization (WHO), Chinese Center for Disease Control and Prevention (CCDC), National Health Commission of the People’s Republic of China, USA National Institutes of Health Ongoing Trials Register (ClinicalTrials.gov), the ISRCTN registry, Google Scholar and the preprint servers medRxiv (https://www.medrxiv.org/), bioRxiv (https://www.biorxiv.org/) and SSRN (https://www.ssrn.com/index.cfm/en/).
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    The bibliographic software EndNote was used and any discrepancies were settled by discussion, consulting a third reviewer (Q Zhou) if necessary.
    EndNote
    suggested: (EndNote, RRID:SCR_014001)
    For randomized controlled trials (RCTs), we will assess the risk of bias independently using Cochrane risk-of-bias tool (17).
    Cochrane
    suggested: (Cochrane Library, RRID:SCR_013000)
    All analyses were performed in STATA version 14.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: This rapid review has several strengths. First, although this is not the first systemic review about the clinical characteristics of children with COVID-19, but is to our knowledge the first to combine the results with meta-analysis and GRADE evaluation of the quality of main evidence, which is of great importance for clinicians to diagnose and treat children rapidly. Second, our study points out the loopholes in some current guidance documents that suggest the diagnosis of suspected cases - also in children - based on the lymphocyte count. Third, as a rapid review, this study summarizes the latest published information on clinical cases, which provides relatively high-quality evidence for the formulation of clinical practice guidelines in the rapidly evolving public health emergency situation and helps policy-makers to make evidence-based decisions quickly (107). Our study has also some limitations. First, due to the rapid fermentation of the public health emergency and new cases emerging continuously, the findings of this review may get outdates relatively soon. Second, cannot be sure if some cases were included in multiple studies. Third, at present, there is no unified definition for clinical classification of the severity of COVID-19, so we had to combine light, mild and moderate disease into one category (mild), while severe and critical cases were both considered as severe cases. Future implications: The researchers should aim to conduct more...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.21037/atm-20-3302
  3. Version published to 10.1101/2020.04.13.20064352v1 on medRxiv