Evidence on the role of journal editors in the COVID19 infodemic: metascientific study analyzing COVID19 publication rates and patterns

  1. Gabriel Grisi
  2. Joao de Deus Barreto Segundo
  3. Camila Veronica Freire
  4. Denise Matias
  5. Mariana Cruz
  6. Larrie Rabelo Laporte
  7. Daniel Medina
  8. Thiago Masashi Taniguchi
  9. Leticia Requiao
  10. Bruno Goes
  11. Luis Claudio Lemos Correia

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Abstract

ABSTRACT Objective: Infodemic, a neologism characterizing an excess of fast-tracked low quality publications, has been employed to depict the scientific research response to the COVID19 crisis. The concept relies on the presumed exponential growth of research output. This study aimed to test the COVID19 infodemic claim by assessing publication rates and patterns of COVID19-related research and a control, a year prior. Design: A Reproduction Number of Publications (Rp) was conceived. It was conceptualized as a division of a week incidence of publications by the average of publications of the previous week. The publication growth rates of preprint and MEDLINE-indexed peer-reviewed literature on COVID19 were compared using the correspondent Influenza output, a year prior, as control. Rp for COVID19 and Influenza papers and preprints were generated and compared and then analyzed in light of the respective growth patterns of their papers and preprints. Main outcomes: Output growth rates and Reproduction Number of Publications (Rp). Results: COVID19 peer-reviewed papers showed a fourteen fold increase compared to Influenza papers. COVID19 papers and preprints displayed an exponential growth curve until the 20th week. COVID19 papers displayed Rp=3.17±0.72, while the control group presented Rp=0.97±0.12. Their preprints exhibited Rp=2.18±0.54 and Rp=0.97±0.27 respectively, with no evidence of exponential growth in the control group, as its Rp remained approximately one. Conclusions: COVID19 publications displayed an epidemic pattern. As the growth patterns of COVID19 peer-reviewed articles and preprints were similar, and the majority of the COVID19 output came from indexed journals, not only authors but also editors appear to had played a significant part on the infodemic. Review protocol: https://osf.io/q3zkw/?view_only=ff540dc4630b4c6e9a2639d732047324 Ethical aspects: No ethical clarence was required as all analyzed data were publicly available.

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  1. ScreenIT

    SciScore for 10.1101/2022.01.23.22269716: (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
    Query: For articles from PubMed-indexed journals, Entrez Direct (EDirect) NIH/NLM application was employed for retrieving publication metadata from MEDLINE by E-utilities API29,30.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    Preprint metadata was retrieved from the bioRxiv and medRxiv servers31, presumed to be the most prominent among the life sciences servers to this date21,22,28.
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Extraction and internal validity assessment: EDirect metadata from COVID19 and Influenza journal articles was downloaded into a Microsoft Excel readable
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Thus, it was decided PubMed was more reliable for an accurate coverage.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Attribute variable PubmedPubDate@pubmed, a date that reflects inclusion in PubMed database, and PubDate, a date that reflects the issue date of publication, were extracted from PubMeb as quality control of ArticleDate, a variable which corresponds to the date the journal publisher has made an electronic version of the article available online.
    PubMeb
    suggested: None
    Exponential model function was described using trendline equation in Excel.
    Excel
    suggested: None
    Statistical analysis and curve estimations were executed in SPSS Statistics version 14 (International Business Machines Corporation, United States of America), graphics were generated using GraphPad Prism 8.0.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

    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.

  2. Version published to 10.1101/2022.01.23.22269716v1 on medRxiv