High admission blood glucose independently predicts poor prognosis in COVID-19 patients: A systematic review and dose-response meta-analysis

  1. Gilbert Lazarus
  2. Jessica Audrey
  3. Vincent Kharisma Wangsaputra
  4. Alice Tamara
  5. Dicky L. Tahapary

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Abstract

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  1. Version published to 10.1016/j.diabres.2020.108561
  2. ScreenIT

    SciScore for 10.1101/2020.09.25.20200774: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    BlindingSearch strategy: Two independent investigators performed thorough literature searches, with discrepancies resolved by a third investigator in a blinded fashion.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    This systematic review was conducted in accordance with the guideline recommended by the Cochrane Prognosis Methods Group5 and reported based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement6.
    Cochrane Prognosis
    suggested: None
    Searches were conducted through PubMed
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    , Ovid EMBASE, CENTRAL,
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    8 Included studies were further assessed for methodological quality by using the Quality in Prognosis Studies tool9 and subsequently judged to be yielding low, moderate, or high risk of bias (Appendix Table S3).
    Quality in Prognosis Studies
    suggested: None
    0 (R Foundation for Statistical Computing, Vienna, Austria)16, and additional analyses with MetaXL software ver.5.3. (EpiGear International, Queensland, Australia)17.
    MetaXL
    suggested: None

    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 10.1101/2020.09.25.20200774v1 on medRxiv