ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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Examining the Relationship between COVID-19 Vaccinations and Reported Incidence
This article has 3 authors:Reviewed by ScreenIT
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Assessment of Virological Contributions to COVID-19 Outcomes in a Longitudinal Cohort of Hospitalized Adults
This article has 18 authors:Reviewed by ScreenIT
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Increased frequency of recurrent in-frame deletions in new expanding lineages of SARS CoV-2 reflects immune selective pressure
This article has 5 authors:Reviewed by ScreenIT
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Predicted impact of the viral mutational landscape on the cytotoxic response against SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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Disinfection of SARS-CoV-2 using UVC reveals wavelength sensitivity contributes towards rapid virucidal activity
This article has 6 authors:Reviewed by ScreenIT
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Efficacy and safety of cyclosporine in the management of coronavirus disease 2019: A protocol for systematic review and meta-analysis
This article has 3 authors:Reviewed by ScreenIT
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Will Vaccine-derived Protective Immunity Curtail COVID-19 Variants in the US?
This article has 3 authors:Reviewed by ScreenIT
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Reduced COVID-19 hospitalizations among New York City residents following age-based SARS-CoV-2 vaccine eligibility: Evidence from a regression discontinuity design
This article has 9 authors:Reviewed by ScreenIT
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Importance of vaccine action and availability and epidemic severity for delaying the second vaccine dose
This article has 7 authors:Reviewed by ScreenIT
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Impact of regional heterogeneity on the severity of COVID-19
This article has 6 authors:Reviewed by ScreenIT