Detection of SARS-CoV-2 variants by Abbott molecular, antigen, and serological tests

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Abstract

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  1. SciScore for 10.1101/2021.04.24.21256045: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Anonymized leftover patient nasopharyngeal swab samples in viral transport media (VTM), collected at the point of discard from inpatients at Guys’ and St Thomas’ Hospital in London (UK Research Ethics Committee 20/SC/0310), were sequenced using the ARTIC protocol [13] on Nanopore instruments to determine infection with the B.1.1.7 variant, then run neat or diluted 5-62.5x before molecular testing on the Abbott ID NOW COVID-19, m2000 RealTime SARS-CoV-2, Alinity m SARS-CoV-2, and Alinity m Resp-4-Plex.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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.

    Results from scite Reference Check: We found no unreliable references.


    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.