Gender disparities in coronavirus disease 2019 clinical trial leadership

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Abstract

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  1. SciScore for 10.1101/2020.08.02.20166751: (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 variableThis tool has been used to predict the gender of first names in studies regarding gender bias [12, 13] and achieves a minimum accuracy of 82%, with an F1 score of 90% for women and 86% for men [14].

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We systematically searched https://clinicaltrials.gov/ and retrieved all clinical trials on COVID-19 registered from January 1, 2020 to June 26, 2020 using “COVID” as a keyword.
    https://clinicaltrials.gov/
    suggested: (ClinicalTrials.gov, RRID:SCR_002309)

    Results from OddPub: Thank you for sharing your data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our analysis has some limitations. We could include only ∼50-75% of trials for which an investigator’s gender could be algorithmically predicted. Furthermore, while such algorithms allow for the rapid analysis of gender disparities such as those conducted here, they can also be exclusionary to gender non-conforming, non-binary, and trans individuals. Because of this, we have taken care to use the term “predicted gender” throughout the text whenever discussing algorithm-based gender assignment in acknowledgement of gender non-conforming, non-binary, and trans investigators. Beyond these limitations, we did not consider studies that received private funding, which may not have been registered on clinicaltrials.gov; however, it is worth noting that clinicaltrials.gov is an international database with widespread international representation. In summary, while the COVID-19 pandemic has thus far provided many new opportunities for research, with numerous clinical trials initiated worldwide, a disproportionate proportion of PIs leading COVID-19 related studies are predicted to be men, despite women accounting for 70% of the global health workforce [16]. Our demonstration of gender differences in trial leadership and grant allocation argue for revised policies and strategies that encourage the participation of women in pandemic research. Not only can these women drive discovery and innovation, but they can act to address health disparities and provide role models for the next generat...

    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

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