Risk factors of the severity of COVID‐19: A meta‐analysis

  1. Abdur Rahman
  2. Nusrat Jahan Sathi

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Abstract

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  1. Version published to 10.1111/ijcp.13916
  2. ScreenIT

    SciScore for 10.1101/2020.04.30.20086744: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Both of the authors searched through PubMed, Google Scholar, EMBASE, and Cochrane Library using keywords: “COVID-19”, “Novel Coronavirus”, “COVID-19 characteristics”, “COVID-19 patient”, and “China coronavirus”.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Microsoft Excel 2013 was used to create metadata, get their univariate characteristics, and export to other software for further analysis.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  3. Version published to 10.1101/2020.04.30.20086744v2 on medRxiv
  4. Version published to 10.1101/2020.04.30.20086744v1 on medRxiv