Testing Healthcare Workers Exposed to COVID19 using Rapid Antigen Detection

  1. Victor Herrera
  2. Vincent Hsu
  3. Ademola Adewale
  4. Lee Johnson
  5. Timothy Hendrix
  6. Jeffrey Kuhlman
  7. Neil Finkler

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Abstract

There is a need to develop safe and cost-effective ways to test healthcare workers for COVID19. Here we describe a rapid antigen testing strategy in a cohort of 497 Healthcare workers exposed to SARS-CoV-2 that can be applied by systems facing a surge of COVID19 cases, increased number of exposures in their workforce and limited RT-PCR availability. Our findings support an expanded use for antigen testing beyond its current indication and highlights the importance of further evaluating this modality for the diagnosis of COVID19 on asymptomatic individuals.

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  1. Version published to 10.1101/2020.08.12.20172726v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.08.12.20172726: (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: 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 10.1101/2020.08.12.20172726v1 on medRxiv