Early chest computed tomography to diagnose COVID-19 from suspected patients: A multicenter retrospective study

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.03.24.20042432: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Ethics of Committees of local hospital.
    Consent: Informed consent for this retrospective study was waived. 2.3.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableExclusion criteria included: (1) past history with chronic lung disease; (2) pregnant women.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: The data of patients were recorded by Epidata and statistical analysis was performed on SPSS 13.0 (IBM Corporation).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    There are several limitations in our study. First, the method is to compare the difference of CT imaging features between confirmed patients and non-confirmed patients of COVID-19. Some early lesions might not be visible on chest CT since the maximum incubation period is 14 days or even more[6]. So the expression of time may not be precise enough according to the development of this disease. Second, the current gold standard for the diagnosis of COVID-19 is merely RT-PCR test[6]. Since the test samples are mostly pharynx swabs rather than bronchoalveolar lavage fluid (BALF), it might still present false negative results after repetitions. In summary, the manifestations of COVID-19 vary. Some patients show similar manifestations with common viral pneumonias. However, it still has specific imaging characteristics. Moreover, the combinations of GGO could be useful in the identification and differential diagnosis of COVID-19, which alerts clinicians to isolate patients for treatment promptly and repeat RT-PCR tests until incubation ends.

    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.