COVID-19: a crash test for biomedical publishing?

  1. Ivan Y Iourov
  2. Maria A Zelenova
  3. Svetlana G Vorsanova

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Abstract

The effect of COVID-19 on biomedical publishing (BP) (i.e. scientific biomedical periodicals continuously published by research communities or commercial publishers) has not been deeply explored. To estimate the immediate COVID-19 impact on BP, we have assessed P ub M ed- i ndexed a rticles about C OVID-19 (PMIAC) from December 2019 to April 2020. PMIAC have been classified according to publication date, country, and journals for evaluation of time-, region- and scientometric-dependant impact of COVID-19 on BP and have been curated manually (i.e. each entry has been individually analyzed). PMIAC analysis reflects geographic and temporal parameters of outbreak spread. A major BP problem is related to the fact that only 40% of articles report/review/analyze data. Another BP weakness is the clusterization of “highly-trusted” publications according to countries of origin and “highly impacting” journals. Finally, a problem highlighted by COVID-19 crisis is the increased specification of biomedical research. To solve the problem, analytical reviews integrating data from different areas of biology and medicine are required. The data on PMIAC suggest priority of “what is published” over “where it is published” and “who are the authors”. We believe that our brief analysis may help to shape forthcoming BP to become more effective in solving immediate problems resulted from global threats.

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  1. Version published to 10.1101/2020.06.13.20130310v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.06.13.20130310: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.06.13.20130310v1 on medRxiv