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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Point-of-Care Lung Ultrasound Predicts Severe Disease and Death Due to COVID-19: A Prospective Cohort Study
This article has 24 authors:Reviewed by ScreenIT
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Combining antigenic data from public sources gives an early indication of the immune escape of emerging virus variants
This article has 6 authors:Reviewed by ScreenIT
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RG203KR Mutations in SARS-CoV-2 Nucleocapsid: Assessing the Impact Using a Virus-Like Particle Model System
This article has 8 authors:Reviewed by ScreenIT
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Longitudinal serologic and viral testing post–SARS-CoV-2 infection and post-receipt of mRNA COVID-19 vaccine in a nursing home cohort—Georgia, October 2020‒April 2021
This article has 26 authors:Reviewed by ScreenIT
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The Canadian COVID-19 Experiences Project: Design and Protocol
This article has 11 authors:Reviewed by ScreenIT
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Estimating the Direct Disability-Adjusted Life Years Associated With SARS-CoV-2 (COVID-19) in the Republic of Ireland: The First Full Year
This article has 6 authors:Reviewed by ScreenIT
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Comparison of outcomes from COVID infection in pediatric and adult patients before and after the emergence of Omicron
This article has 6 authors:Reviewed by ScreenIT
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Use of Andrographis paniculata (Burm.f.) Wall. ex Nees and risk of pneumonia in hospitalised patients with mild coronavirus disease 2019: A retrospective cohort study
This article has 8 authors:Reviewed by ScreenIT
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Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions
This article has 17 authors:Reviewed by ScreenIT
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SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway
This article has 694 authors:Reviewed by ScreenIT