Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach

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Abstract

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  1. SciScore for 10.1101/2021.06.18.21259157: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All the different analyses for pre-processing, machine learning approaches, and visualization were performed with custom scripts in the RStudio software (Version 1.1.453, https://www.rstudio.com/) with the R software (Version 3.6.3, https://www.r-project.org/) in local servers of the Universidad de Costa Rica.
    RStudio
    suggested: (RStudio, RRID:SCR_000432)
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    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:
    Our analyses presented some limitations that must be taken into account in the interpretation of results: (1) classification of positive cases of COVID-19 was based on the positivity of a PCR for nasopharyngeal samples, i.e., we depended on the performance of the test and sample quality; (2) records were retrieved from a local database with some missing information, mainly for SARS-CoV-2 genomic data; and (3) symptoms of very low frequency, social behavior, or genetic factors of the host were not considered in this study. Finally, due to vaccination started massively in January 2021 in Costa Rica (although the first doses were applied at the end of December 2020), we consider that this study represents a special work to give the panorama of COVID-19 in pre-vaccination time (2020). In future work, we hope to assess the vaccination status and how this event has impacted the clinical profiles of COVID-19 patients during 2021.

    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.