Lopinavir-Ritonavir in the Treatment of COVID-19: A Dynamic Systematic Benefit-Risk Assessment

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Abstract

No abstract available

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  1. SciScore for 10.1101/2020.05.27.20114470: (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

    No key resources detected.


    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: A strength of this approach is the inclusion of all key benefits and risks in the same model and a transparent framework into which further data can be included as and when this becomes available. The method has a significant advantage compared to systematic reviews which are equally comprehensive but focus only on efficacy. When sufficient data is available, the methodology allows benefits and risks to be ranked, and weightings applied based on this ranking, with further quantitative analysis. The reproducibility of this assessment allows multiple treatments to be assessed using this approach, thereby allowing direct comparison between different treatments. This is of great significance during the current COVID-19 crisis, in which several potential interventions currently under evaluation need to be assessed and evaluated in real time, and where new data needs to be incorporated quickly. Regulatory decision makers are also familiar with this framework, facilitating interpretation. A limitation of the benefit-risk assessment presented at this time is the relative paucity of data that has been included in the model, as many clinical trials assessing LPVr are still ongoing. In addition, trials for which data were available had very small sample sizes and were likely to be underpowered when examining non-primary outcomes. Study quality was also not considered in the assessment, although we only included peer-reviewed manuscripts. 5.2 Conclusions: B...

    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.