Association between self-reported signs and symptoms and SARS-CoV-2 antibody detection in UK key workers

  1. Ranya Mulchandani
  2. Sian Taylor-Philips
  3. Hayley E. Jones
  4. A.E. Ades
  5. Ray Borrow
  6. Ezra Linley
  7. Peter D Kirwan
  8. Richard Stewart
  9. Philippa Moore
  10. John Boyes
  11. Anil Hormis
  12. Neil Todd
  13. Antoanela Colda
  14. Ian Reckless
  15. Tim Brooks
  16. Andre Charlett
  17. Matthew Hickman
  18. Isabel Oliver
  19. David Wyllie

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Abstract

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  1. Version published to 10.1016/j.jinf.2021.03.019
  2. ScreenIT

    SciScore for 10.1101/2020.08.19.20178186: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: If they agreed to take part, they completed an online consent form and epidemiological questionnaire (Supplementary Material), regarding their previous exposures, whether they believed they had had COVID-19, the date on which the first such illness began (where they could report any combination of a wide range of symptoms), any previous PCR or antibody test for SARS-CoV-2 (and results), and other details including any illness in their household.
    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: 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 10.1101/2020.08.19.20178186 on medRxiv