A comparison of emergency department presentations for medically unexplained symptoms in frequent attenders during COVID-19

  1. Natasha F Daniels
  2. Raiiq Ridwan
  3. Ed BG Barnard
  4. Talha M Amanullah
  5. Catherine Hayhurst

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.7861/clinmed.2020-1093
  2. ScreenIT

    SciScore for 10.1101/2020.08.25.20181511: (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

    Software and Algorithms
    SentencesResources
    Data analysis: Data were analysed in Prism for macOS (v.8.4.2, GraphPad Software, San Diego, CA, USA); presented as n(%), % (95% confidence interval (95%CI) – Wilson/Brown method).
    GraphPad
    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: We detected the following sentences addressing limitations in the study:
    Limitations: The data analysis was performed retrospectively and represents a single ED, thus the results are not necessarily translatable to other centres. Additionally, due to the perceived increase in MUS patients before commencing the data extraction, reviewer bias is an important consideration. This was minimised by having two reviewers (ND and RR) extract the data from both 2019 and 2020.

    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 10.1101/2020.08.25.20181511v1 on medRxiv