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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Syndromic Surveillance-Based Estimates of Vaccine Efficacy Against COVID-Like Illness from Emerging Omicron and COVID-19 Variants
This article has 4 authors:Reviewed by ScreenIT
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COVID-19 endgame: from pandemic to endemic? Vaccination, reopening and evolution in a well-vaccinated population
This article has 5 authors:Reviewed by ScreenIT
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Differences in Outcomes and Factors Associated With Mortality Among Patients With SARS-CoV-2 Infection and Cancer Compared With Those Without Cancer
This article has 6 authors:Reviewed by ScreenIT
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Modeling Vaccine Efficacy for COVID-19 Outbreak in New York City
This article has 4 authors:Reviewed by ScreenIT
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An infectious SARS-CoV-2 B.1.1.529 Omicron virus escapes neutralization by therapeutic monoclonal antibodies
This article has 10 authors:Reviewed by ScreenIT
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Amyloidogenesis of SARS-CoV-2 Spike Protein
This article has 2 authors:Reviewed by ScreenIT
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A third vaccination with a single T cell epitope protects against SARS-CoV-2 infection in the absence of neutralizing antibodies
This article has 26 authors:Reviewed by ScreenIT
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A recurring YYDRxG pattern in broadly neutralizing antibodies to a conserved site on SARS-CoV-2, variants of concern, and related viruses
This article has 8 authors:Reviewed by ScreenIT
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Striking antibody evasion manifested by the Omicron variant of SARS-CoV-2
This article has 23 authors:Reviewed by ScreenIT
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Geographic disparities and determinants of COVID-19 incidence risk in the greater St. Louis Area, Missouri (United States)
This article has 7 authors:Reviewed by ScreenIT