Increasing SARS-CoV-2 RT-qPCR testing capacity by sample pooling

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Abstract

No abstract available

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  1. SciScore for 10.1101/2020.10.24.20218685: (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

    Software and Algorithms
    SentencesResources
    Sensitivity and the 95% Confidence Interval (CI) was assessed with MedCalc (MedCalc Software Ltd.).
    MedCalc
    suggested: (MedCalc, RRID:SCR_015044)

    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:
    A major limitation of the study is that sensitivity was only evaluated in the pilot stage. However, despite the small Ct shift between the positive pools and the individual positive samples in the prospective study, a high impact of false negatives is not expected (Cherif et al. 2020). Our data suggest that our sample pooling strategy is able to detect COVID-19 positive cases with Ct values ≤39.4. Pooling with compressive sampling designs, which involves repetitive tests (Shental et al. 2020), and the use of alternative testing approximations (Peto et al. 2020) may help to further lessen the impact of the dilution factor on pooling and continue increasing health capacity building.

    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.