COVID-19 in people with neurofibromatosis 1, neurofibromatosis 2, or schwannomatosis

  1. Jineta Banerjee
  2. Jan M. Friedman
  3. Laura J. Klesse
  4. Kaleb H. Yohay
  5. Justin T. Jordan
  6. Scott R. Plotkin
  7. Robert J. Allaway
  8. Jaishri O. Blakeley

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Abstract

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  1. Version published to 10.1016/j.gim.2022.10.007
  2. ScreenIT

    SciScore for 10.1101/2022.03.31.22273208: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    While it is an extensive collection of EHR data, various limitations exist in using this dataset to determine SARS-CoV-2 and COVID-19-related risks for the global population. For example, we observed a greater prevalence of NF1 patients in the N3C as compared to population prevalence estimates, indicating that this dataset may not represent the general population due to its specific data acquisition protocols. As with any multi-site combination of EHR data, there may also be site-related differences in clinical measures due to variations in clinical practice and medical record documentation. Any biases affecting analyses of care patterns or outcomes due to geographical, regional, cultural, or other differences between institutions remained unassessed due to anonymized coding of the N3C data contributing institutions in the de-identified dataset. Furthermore, clinical coding of patients with NF1, NF2, SWN or other rare diseases may be incomplete in EHRs, i.e. the codes used to define the study cohorts (Supplemental Tables 1-9) within N3C could have missed disease-positive patients without appropriately recorded diagnostic codes. Lastly, the SARS-CoV-2 testing rate, diagnosis and treatment of COVID-19, access to clinical care in various sites and various disease populations may vary significantly. Additionally, there are various considerations specific to the data deposited in the N3C data enclave. Due to N3C’s phenotype acquisition design, the patients included in the Data Enc...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2022.03.31.22273208v1 on medRxiv