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  1. Review 1: "Evaluation of a Machine Learning Approach Utilizing Wearable Data for Prediction of SARS-CoV-2 Infection in Healthcare Workers"

    This study develops a prediction model for positive COVID-19 diagnosis using data collected from Apple Watches on heart rate variability (HRV) among healthcare workers. Reviewers highlight unclear model justifications and methodology.

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  2. Review 2: "Evaluation of a Machine Learning Approach Utilizing Wearable Data for Prediction of SARS-CoV-2 Infection in Healthcare Workers"

    This study develops a prediction model for positive COVID-19 diagnosis using data collected from Apple Watches on heart rate variability (HRV) among healthcare workers. Reviewers highlight unclear model justifications and methodology.

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  4. SciScore for 10.1101/2021.11.04.21265931: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Subjects completed daily surveys to report any COVID-19 related symptoms, symptom severity, the results for any SARS-CoV-2 nasal PCR tests, and SARS-CoV-2 antibody test results.
    SARS-CoV-2
    suggested: None

    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 to our study. First, HRV was collected sporadically by the Apple Watch. We employed statistical modeling to account for this. However, a denser data set using continuous data would likely further improve our predictions. Second, the model we employed used a 7-day smoothing approach. This approach observed infection-induced changes in HRV later than if HRV was estimated using a single-day method. Thus, the approach we employed is conservative. An additional limitation is that the Apple Watch provides HRV measurements only in the SDDN time domain. This limits assessments between other types of HRV measurements and COVID-19 outcomes. Additionally, other factors might impact HRV, which we were not able to capture and control for in the analysis. Furthermore, we were not routinely checking for SARs-CoV-2 infections and relied on subjects reporting a COVID-19 diagnosis. Therefore, infections could have occurred that are not accounted. Lastly, we did not externally validate our machine learning algorithm in another cohort.

    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.

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