Semantic and Geographical Analysis of COVID-19 Trials Reveals a Fragmented Clinical Research Landscape Likely to Impair Informativeness

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Abstract

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  1. SciScore for 10.1101/2020.05.14.20101758: (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
    Databases: Data were downloaded from clinicaltrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP https://www.who.int/ictrp/en/) on April 11th and 27th Data for Covid cases by country and for US states were downloaded from Johns Hopkins Data Repository (https://github.com/CSSEGISandData/COVID-19) and for Italian regions from Presidenza del Consiglio dei Ministri – Dipartimento della Protezione Civile (https://github.com/pcm-dpc/COVID-19) on April 27th Details on ontology definition, geographical analyses and statistical analyses are discussed in supplementary methods
    https://www.who.int/ictrp/en/
    suggested: (WHO International Clinical Trials Registry Platform, RRID:SCR_004475)

    Results from OddPub: Thank you for sharing your code and data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The main limitation of our analysis is represented by the heterogeneity in quality and quantity of available information. The source databases use often non-overlapping trial categorization methods and many of the records have missing, mis-spelled or imprecise wording, potentially causing relevant selection biases which we attempted to mitigate by forcing information through controlled vocabularies, a procedure which may results in loss of information. We argue that several of the planned trials are unlikely to provide high-quality results for the following reasons. First and foremost, the unrealistic percentages of total prevalent cases needed to fulfill planned enrollment at the national level implies that several trials are unlikely to reach target sample sizes, with severe loss of statistical power or study termination. This has in fact already been observed with the recently published Remdesivir trial in China, which failed to complete enrolment and led to conflicting interpretations[6]. Geographical fragmentation will magnify local and study-specific confounding in demographics, comorbidities and the availability of healthcare resources, which are known to heavily impact on Covid-19 outcome [7–9]. Variegated endpoints and inclusion criteria will inhibit the possibility to adequately compare and meta-analyse treatments across trials. Proper dose-finding trials are scarce, with the risk of under- or over-treating patients and of underestimating potentially risky drug-inte...

    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.