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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COVID-19 vaccine effectiveness among immunocompromised populations: a targeted literature review of real-world studies
This article has 9 authors:Reviewed by ScreenIT
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Monoclonal antibody treatment drives rapid culture conversion in SARS-CoV-2 infection
This article has 24 authors:Reviewed by ScreenIT
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The Effect of Weather Pattern on the Second Wave of Coronavirus: A cross study between cold and tropical climates of France, Italy, Colombia, and Brazil
This article has 1 author:Reviewed by ScreenIT
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Predicting the course of Covid-19 and other epidemic and endemic disease
This article has 2 authors:Reviewed by ScreenIT
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Evaluation of the Cepheid Xpert Xpress SARS-CoV-2 test for bronchoalveolar lavage
This article has 4 authors:Reviewed by ScreenIT
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Neutralizing antibodies against the SARS-CoV-2 Delta and Omicron variants following heterologous CoronaVac plus BNT162b2 booster vaccination
This article has 26 authors:Reviewed by ScreenIT
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Sociodemographic factors and self-restraint from social behaviors during the COVID-19 pandemic in Japan: A cross-sectional study
This article has 9 authors:Reviewed by ScreenIT
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Risk of Cardiovascular Events After COVID-19
This article has 35 authors:Reviewed by ScreenIT
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SARS-CoV-2 Omicron-B.1.1.529 Variant leads to less severe disease than Pango B and Delta variants strains in a mouse model of severe COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Structural basis of SARS-CoV-2 Omicron immune evasion and receptor engagement
This article has 17 authors:Reviewed by ScreenIT