COVID-19 in multiple sclerosis patients and risk factors for severe infection

  1. Farhan Chaudhry
  2. Helena Bulka
  3. Anirudha S. Rathnam
  4. Omar M. Said
  5. Jia Lin
  6. Holly Lorigan
  7. Eva Bernitsas
  8. Jacob Rube
  9. Steven J. Korzeniewski
  10. Anza B. Memon
  11. Phillip D. Levy
  12. Lonni Schultz
  13. Adil Javed
  14. Robert Lisak
  15. Mirela Cerghet

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Abstract

No abstract available

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  1. Version published to 10.1016/j.jns.2020.117147
  2. ScreenIT

    SciScore for 10.1101/2020.05.27.20114827: (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:
    A significant limitation to our study is the relatively small sample size collected; therefore risk-stratification adjusting for confounders was not possible. Larger studies are therefore required in order to improve for confounding variables. Also, there was a significant discrepancy in sample-size for each course, as there were only 6 patients with severe disease course. Therefore, significant differences may not have been detected in post hoc comparisons with this course. Regardless, we showed that certain MS-characteristics were more frequent in more COVID-19 severe courses.

    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 10.1101/2020.05.27.20114827v1 on medRxiv