Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation

This article has been Reviewed by the following groups

Read the full article

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.05.07.20094573: (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 our local institutional review board (IRB# 2000027747).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The gradient-boosting CSI model was fit using the XGBoost package and hyperparameters were set using Bayesian optimization with a tree-structured Parzen estimator (Supplementary Materials).24, 25 All analyses were performed in Python (version 3.8.2).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 in this study were observational data provided from a single health system and so may not be generalizable based on local testing and admissions practices. Our data were extracted from an electronic health record, which is associated with known limitations including propagation of old or incomplete data. Similarly, there are important markers of oxygenation which were out of the scope of our study, including alveolar-arterial gradients. Retrospective observational studies lack control of variables so prospective studies will be required to assess validity of the presented models and the specificity of the features we identify as important to COVID-19 progression. Assumptions were made in data processing where noted in the methods, which introduce biases into our results. Chest x-ray interpretation was done manually using radiology reports, but without reviewing the radiography, which introduces subjectivity as reflected in the inter-rater agreement metric. Most significant, however, is that management of COVID-19 is evolving, so it may be possible that future clinical decisions may not match those standards used in the reported clinical settings.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04292899CompletedStudy to Evaluate the Safety and Antiviral Activity of Remde…
    NCT04292899CompletedStudy to Evaluate the Safety and Antiviral Activity of Remde…


    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.