The impact of early scientific literature in response to COVID-19: a scientometric perspective

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Abstract

Background

In the early phases of a new pandemic, identifying the most relevant evidence and quantifying which studies are shared the most can help researchers and policy makers. The aim of this study is to describe and quantify the impact of early scientific production in response to COVID-19 pandemic.

Methods

The study consisted of: 1) review of the scientific literature produced in the first 30 days since the first COVID-19 paper was published; 2) analysis of papers’ metrics with the construction of a “Computed-Impact-Score” (CIS) that represents a unifying score over heterogeneous bibliometric indicators. In this study we use metrics and alternative metrics collected into five separate categories. On top of those categories we compute the CIS. Highest CIS papers are further analyzed.

Results

239 papers have been included in the study. The mean of citations, mentions and social media interactions resulted in 1.63, 10 and 1250, respectively. The paper with highest CIS resulted “ Clinical features of patients […]” by Chaolin Huang et al., which rated first also in citations and mentions. This is the first paper describing patients affected by the new disease and reporting data that are clearly of great interest to both the scientific community and the general population.

Conclusions

The early response of scientific literature during an epidemic does not follow a pre-established pattern. Being able to monitor how communications spread from the scientific world toward the general population using both traditional and alternative metric measures is essential, especially in the early stages of a pandemic.

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  1. SciScore for 10.1101/2020.04.15.20066183: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Although the virus name was updated to SARS-CoV-2 by the International Committee on Taxonomy of Viruses on February 11th 2020 (Gorbalenya 2020), we performed the search using the term “nCoV” because it was presumed that no one, between February 11 and 13, would have used the term “SARS-COV-2”.
    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.