Frequency of Neurologic Manifestations in COVID-19

  1. Shubham Misra
  2. Kavitha Kolappa
  3. Manya Prasad
  4. Divya Radhakrishnan
  5. Kiran T. Thakur
  6. Tom Solomon
  7. Benedict Daniel Michael
  8. Andrea Sylvia Winkler
  9. Ettore Beghi
  10. Alla Guekht
  11. Carlos A. Pardo
  12. Greta Karen Wood
  13. Sherry Hsiang-Yi Chou
  14. Ericka L. Fink
  15. Erich Schmutzhard
  16. Amir Kheradmand
  17. Fan Kee Hoo
  18. Amit Kumar
  19. Animesh Das
  20. Achal K. Srivastava
  21. Ayush Agarwal
  22. Tarun Dua
  23. Kameshwar Prasad

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Abstract

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  1. Version published to 10.1212/wnl.0000000000012930
  2. ScreenIT

    SciScore for 10.1101/2021.04.20.21255780: (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

    Antibodies
    SentencesResources
    Population: The target population for this review includes patients clinically suspected to have COVID-19 confirmed by real-time reverse-transcription polymerase chain reaction (RT-PCR) detection, high throughput sequencing, SARS-CoV-2 viral culture in throat swab specimens, or SARS-CoV-2 antibody detection in blood samples.
    SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    A comprehensive literature search was carried out for the period 31st December 2019 to 15th December 2020 in PubMed, EMBASE, MEDLINE, Google Scholar, Cochrane Library and ClinicalTrials.gov.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)

    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 of this review: There are several important limitations for this review. Firstly, there are few data from prospective studies; most data came from retrospective cohorts or case series. Additionally, standardized classifications, definitions, and diagnostic criteria of neurological manifestations were not uniformly used or reported. Moreover, studies relating to many manifestations were not suitable for pooling. Thirdly, as the vast majority of data analyzed came from hospitalized cases (89%), our pooled prevalence estimates of neurological manifestations, especially those of severe manifestations such as stroke, may not represent community prevalence. We are therefore unable to draw conclusions regarding the prevalence of neurological manifestations amongst COVID-19 patients in the community. Additionally, interpretation of data regarding pre-existing neurological conditions and COVID-19 diagnoses is limited due to the proportion of hospitalized cases studied. Meta-analyses for most outcomes in this review had a high degree of heterogeneity, which could only partly be explained by meta-regression and subgroup analyses. It is expected that the patient populations in the included studies were clinically diverse, having presented to centers with varied referral pathways, and this may have contributed to the heterogeneity observed. For almost all symptoms, studies with higher risk of bias yielded higher pooled prevalence, indicating overestimation from bias in selecti...

    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 10.1101/2021.04.20.21255780v1 on medRxiv