1. Stephen X Zhang
  2. Shuhua Sun
  3. Asghar Afshar Jahanshahi
  4. Yifei Wang
  5. Abbas Nazarian Madavani
  6. Jizhen Li
  7. Maryam Mokhtari Dinani

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Abstract

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  1. Version published to 10.2147/rmhp.s273755
  2. ScreenIT

    SciScore for 10.1101/2020.04.30.20086348: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The survey was approved (IR.SSRI.REC.1389.685) by the ethics committee of Shahid Rejaee University in Iran.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Analysis: We analyzed the data using STATA 16·0 with a significance level at 0·05.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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:
    This study has several limitations. While the severity of the COVID-19 crisis in Iran early on presents a setting to predict COVID-19 infection status, the number of COVID-19 positive cases in our sample remained relatively small. Similarly, the number of unemployed participants in the sample was too small to enable more analysis. While we aimed to cover a broad spectrum of adults in Iran, our sample should not be taken as a representative national sample. Also, the risk factors of COVID-19 infection are likely to differ across countries given different cultural and social practices. Lastly, our model is predictive, and we do not claim causalities. In summary, this study provides the first attempt to explore predictors of people at greater risk of COVID-19 infection during the COVID-19 pandemic. We hope this research opens a new research avenue by identifying higher risk individuals to enable more targeted infectious disease prevention, communication, testing, and control to curtail the pandemic.

    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 10.1101/2020.04.30.20086348v1 on medRxiv