Sample pooling is a viable strategy for SARS-CoV-2 detection in low-prevalence settings

  1. Brian S.W. Chong
  2. Thomas Tran
  3. Julian Druce
  4. Susan A. Ballard
  5. Julie A. Simpson
  6. Mike Catton

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Abstract

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    SciScore for 10.1101/2020.08.26.20181719: (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: We detected the following sentences addressing limitations in the study:
    This study has several weaknesses. Firstly, it is not possible to design a single generic pooling workflow that is applicable to all laboratories. However, the workflow developed in this study has been successfully implemented in at least one other laboratory (MDU PHL) which used a completely different testing method. This demonstrates the transferability of our workflow. Secondly, the clinical impact of the slight reduction in assay sensitivity imparted by pooling was not formally assessed in this study. However, samples with low pre-test probability were specifically selected for pooling, and it is not possible to fully evaluate the false-negativity rate of pooling without large scale parallel testing of individual samples. Additionally, upon reviewing data from the first pooling period in March 2020, we found no instances where a patient with a negative result on pooled testing subsequently tested positive on repeat swab within seven days.

    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.pathol.2020.09.005
  3. Version published to 10.1101/2020.08.26.20181719 on medRxiv