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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Clinical features and management of severe COVID-19: A retrospective study in Wuxi, Jiangsu Province, China
This article has 13 authors:Reviewed by ScreenIT
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Multi-Stage Group Testing Improves Efficiency of Large-Scale COVID-19 Screening
This article has 3 authors:Reviewed by ScreenIT
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Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study
This article has 16 authors:Reviewed by ScreenIT
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Immunological assays for SARS-CoV-2: an analysis of available commercial tests to measure antigen and antibodies
This article has 7 authors:Reviewed by ScreenIT
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A Simple Colorimetric Molecular Detection of Novel Coronavirus (COVID-19), an Essential Diagnostic Tool for Pandemic Screening
This article has 6 authors:Reviewed by ScreenIT
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Molecular Mechanism of Action of Repurposed Drugs and Traditional Chinese Medicine Used for the Treatment of Patients Infected With COVID-19: A Systematic Scoping Review
This article has 6 authors:Reviewed by ScreenIT
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Global COVID-19 transmission rate is influenced by precipitation seasonality and the speed of climate temperature warming
This article has 2 authors:Reviewed by ScreenIT
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Acceptance of and preference for COVID-19 vaccination in healthcare workers: a comparative analysis and discrete choice experiment
This article has 10 authors:Reviewed by ScreenIT
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ACE inhibition and cardiometabolic risk factors, lung ACE2 and TMPRSS2 gene expression, and plasma ACE2 levels: a Mendelian randomization study
This article has 33 authors:Reviewed by ScreenIT
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Excess cases of influenza and the coronavirus epidemic in Catalonia: a time-series analysis of primary-care electronic medical records covering over 6 million people
This article has 6 authors:Reviewed by ScreenIT