Development and validation of a knowledge-driven risk calculator for critical illness in COVID-19 patients

  1. Amos Cahan
  2. Tamar Gottesman
  3. Michal Tzuchman Katz
  4. Roee Masad
  5. Gal Azulay
  6. Dror Dicker
  7. Aliza Zeidman
  8. Evgeny Berkov
  9. Boaz Tadmor
  10. Shaul Lev

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.06.14.20103754: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: The study was approved and informed consent waived by the Rabin Medical Center Institutional Review Board (ref. 0339-20-RMC).
    IRB: The study was approved and informed consent waived by the Rabin Medical Center Institutional Review Board (ref. 0339-20-RMC).
    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:
    Another limitation is the lack of information on some laboratory tests which were included in the original analysis, however this could have weakened our results.

    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.

  2. Version published to 10.1016/j.ajem.2020.09.051
  3. Version published to 10.1101/2020.06.14.20103754v1 on medRxiv