Remdesivir in Treatment of COVID-19: A Systematic Benefit–Risk Assessment

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Abstract

No abstract available

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  1. SciScore for 10.1101/2020.05.07.20093898: (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
    BRAT uses a six step iterative process to support the decision and communication of a Benefit-Risk Assessment: define decision context, identify outcomes, identify data sources, customise framework, assess outcome importance, and display and interpret key Benefit-Risk metrics [20, 21].
    BRAT
    suggested: (BRAT, RRID:SCR_013159)

    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:
    5.1 Strengths and Limitations: Sample sizes for each outcome were limited to those available in the original studies and may not have adequate power to detect differences in risk between groups, especially where the outcomes examined were not the primary outcome of interest. The benefit-risk assessment is limited by the availability of data in the published literature. However, this assessment can be subsequently updated once further data from clinical trials are available. In addition, given the public health urgency of the COVID-19 pandemic, it is important to provide a systematic assessment of the benefits and risks of remdesivir treatment with evidence available to date and establish a framework which can be used to rapidly update the assessment once further data become available. Data quality is not reflected in this benefit-risk assessment, though all data was extracted from peer-reviewed manuscripts, with the exception of data included from the Adaptive COVID-19 treatment trial. These data were obtained from the NIAID website, and included time to recovery and mortality data. Since the full results of this clinical trial were not made available at the time of datalock (April 30th 2020), a wNCB analysis was not undertaken because there was a concern that only including efficacy outcomes from the Adaptive COVID-19 treatment trial may bias the wNCB results. It is anticipated that such an analysis can be undertaken in the future when results are made available, to provide ...

    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.