A Prognostic Model to Predict Recovery of COVID-19 Patients Based on Longitudinal Laboratory Findings

This article has been Reviewed by the following groups

Read the full article

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.04.04.20053280: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Ethics Committee of the First Hospital of Jilin University, Changchun Infectious Disease Hospital and Siping Infectious Disease Hospital.
    Consent: Written informed consent was waived in review of the emergency need to collect clinical data.
    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:
    Limitations of this study: Disadvantages are as follows: (1) there were 59 cases enrolled in the present study and the sample size may be not very large. Nevertheless, given that Jilin province is the least affected area in China, this study covered approximately 70% of diagnosed cases in Jilin province, in addition to longitudinal measurements of laboratory tests had been collected, these rendered the sample size of this study adequate for reliable statistical estimations; (2) the data of clinical manifestation in the patients with COVID-19 was not included since this study mainly focused on the temporal trajectories of laboratory findings.

    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.