Pre-existing traits associated with Covid-19 illness severity

  1. Joseph E. Ebinger
  2. Natalie Achamallah
  3. Hongwei Ji
  4. Brian L. Claggett
  5. Nancy Sun
  6. Patrick Botting
  7. Trevor-Trung Nguyen
  8. Eric Luong
  9. Elizabeth H. Kim
  10. Eunice Park
  11. Yunxian Liu
  12. Ryan Rosenberry
  13. Yuri Matusov
  14. Steven Zhao
  15. Isabel Pedraza
  16. Tanzira Zaman
  17. Michael Thompson
  18. Koen Raedschelders
  19. Anders H. Berg
  20. Jonathan D. Grein
  21. Paul W. Noble
  22. Sumeet S. Chugh
  23. C. Noel Bairey Merz
  24. Eduardo Marbán
  25. Jennifer E. Van Eyk
  26. Scott D. Solomon
  27. Christine M. Albert
  28. Peter Chen
  29. Susan Cheng

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Abstract

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  1. Version published to 10.1371/journal.pone.0236240
  2. ScreenIT

    SciScore for 10.1101/2020.04.29.20084533: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: The CSMC institutional review board approved all protocols for the current study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All analyses were performed using R, version 3.5.1 (R Foundation for Statistical Computing) and Stata, version 15 (StataCorp).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.04.29.20084533v2 on medRxiv
  4. Version published to 10.1101/2020.04.29.20084533v1 on medRxiv