A bibliometric analysis of rheumatology and COVID-19 researches

  1. Ozge Pasin
  2. Tugce Pasin

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Abstract

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  1. Version published to 10.1007/s10067-021-05844-y
  2. ScreenIT

    SciScore for 10.1101/2021.03.31.21254695: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: The dataset is open source, so there is no need for approval by an ethical committee.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The relationships between citation numbers and some related variables were analyzed with Spearman correlation coefficients, and p-values are reported and tested at a significance level of 0.05 using IBM SPSS Statistics 21.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    There is a limitation: the only source of publications was the WoS core. To the best of our knowledge, this paper is the first bibliometric analysis on rheumatology and COVID-19, which could be useful for future studies.

    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.

  3. Version published to 10.1101/2021.03.31.21254695v1 on medRxiv