Publishing of COVID-19 preprints in peer-reviewed journals, preprinting trends, public discussion and quality issues

  1. Ivan Kodvanj
  2. Jan Homolak
  3. Davor Virag
  4. Vladimir Trkulja

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Abstract

No abstract available

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  1. Version published to 10.1007/s11192-021-04249-7
  2. Version published to 10.1101/2020.11.23.394577v3 on bioRxiv
  3. Version published to 10.1101/2020.11.23.394577v2 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.11.23.394577: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The 2nd objective was to illustrate preprinting trends of COVID-19-related and non-related manuscripts on bioRxiv and medRxiv, their usage statistics and to estimate the extent of the public peer-review (i.e., pre-submission peer-review) using the number of posted comments and Altmetric data as proxies.
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Data collection: All data retrieval and management were done in R (version 4.0.2) (“The R Project for Statistical Computing,” n.d.).
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    To avoid bias due to possible over- or underrepresentation of the COVID-19 articles in PubMed, in comparison with the Retraction Watch database, we repeated the analysis using only data provided by the PubMed database.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The present data support such a view by demonstrating an independent association between COVID-19-related topic and a higher probability of publishing but have two major limitations that preclude straightforward generalizations: a) the analysis was limited only to a subset of all manuscripts “produced” during the observed period, i.e., those that were preprinted. This, however, was the only reasonable choice – these are the only manuscripts whose existence could be clearly verified and for which the risk (probability) of publishing could be (prospectively) estimated; b) the observed period was bounded (January 1 to November 01, 2020), which might have affected the outcomes: our supplemental analysis indicates that it could take up to around 500 days for a preprint to get published (Supplemental Figure S1), hence the present observations might simply reflect a certain lag-time present for non-COVID-19-related preprints. Therefore, the present results pertain and should be interpreted specifically with respect to preprinted manuscripts and the observed period which almost completely overlaps with the duration of the COVID-19 pandemic. We particularly accounted for potential bias arising from unequal “time at risk” by definition of two complementary outcomes differentially affected by the bounded observational period, and by stratification of preprints in respect to preprinting date. With adjustment for several other (albeit not all potentially relevant) potential sources of bia...

    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.

  5. Version published to 10.1101/2020.11.23.394577v1 on bioRxiv