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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A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients
This article has 9 authors:Reviewed by ScreenIT
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Modelling the potential impact of mask use in schools and society on COVID-19 control in the UK
This article has 9 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Changes in Social Behavior Over Time During the COVID-19 Pandemic
This article has 4 authors:Reviewed by ScreenIT
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Analysis of the correlation between anti-MDA5 antibody and the severity of COVID-19: a retrospective study
This article has 16 authors:Reviewed by ScreenIT
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Single-component, self-assembling, protein nanoparticles presenting the receptor binding domain and stabilized spike as SARS-CoV-2 vaccine candidates
This article has 9 authors:Reviewed by ScreenIT
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Role of interfering substances in the survival of coronaviruses on surfaces and their impact on the efficiency of hand and surface disinfection
This article has 10 authors:Reviewed by ScreenIT
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Non-COVID-19 deaths in the United States during the imposition of sheltering-in-place
This article has 4 authors:Reviewed by ScreenIT
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Early behavior of Madrid Covid-19 disease outbreak: A mathematical model
This article has 2 authors:Reviewed by ScreenIT
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Impact of age, ethnicity, sex and prior infection status on immunogenicity following a single dose of the BNT162b2 mRNA COVID-19 vaccine: real-world evidence from healthcare workers, Israel, December 2020 to January 2021
This article has 7 authors:Reviewed by ScreenIT
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Global Geographic and Temporal Analysis of SARS-CoV-2 Haplotypes Normalized by COVID-19 Cases During the Pandemic
This article has 8 authors:Reviewed by ScreenIT