Modeling Donor Screening Strategies to Reduce the Risk of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission via Fecal Microbiota Transplantation
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Abstract
The potential for transmission of severe acute respiratory syndrome coronavirus 2 shed in stool via fecal microbiota transplantation is not yet known, and the effectiveness of various testing strategies to prevent fecal microbiota transplantation-based transmission has also not yet been quantified. In this study, we use a mathematical model to simulate the utility of different testing strategies.
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SciScore for 10.1101/2020.06.24.169094: (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
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, it has multiple limitations. First, as a modeling study, the accuracy of the results depend on the accuracy of the input parameters and the appropriateness of the model structure, especially the tests’ sensitivity and specificity as well as the incidence of SARS-CoV-2 infection, values which remain subject to refinement. Thus, the quantitative predictions made by the model should be used as guides to clinical reasoning rather than as …
SciScore for 10.1101/2020.06.24.169094: (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
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, it has multiple limitations. First, as a modeling study, the accuracy of the results depend on the accuracy of the input parameters and the appropriateness of the model structure, especially the tests’ sensitivity and specificity as well as the incidence of SARS-CoV-2 infection, values which remain subject to refinement. Thus, the quantitative predictions made by the model should be used as guides to clinical reasoning rather than as precision forecasts. Second, the model makes a number of assumptions about the course of disease that may be shown to be invalid or that are no longer applicable. For example, our assumption that newly enrolled donors are seronegative maximizes the sensitivity of serology testing. As the number of candidate donors with positive serology rises, the sensitivity and utility of the serology test will decline. Finally, verifying the model would be challenging, as the possibility of fecal-oral transmission of SARS-CoV-2 has not been confirmed, and there is no accepted “gold standard” for detecting SARS-CoV-2 in stool. Although these results are encouraging, we again caution that they depend on a number of assumptions about testing quality and SARS-CoV-2 epidemiology that will be refined in the coming months. Nevertheless, this method is valuable in assessing the risks of transmission in this evolving pandemic, and we hope this approach can serve as a model for evaluating testing strategies for other pathogens or human-derived therapies beyond ...
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.
- No funding statement was detected.
- No protocol registration statement was detected.
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