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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Phase II Clinical trial for Evaluation of BCG as potential therapy for COVID-19
This article has 5 authors: -
Support and follow-up needs of patients discharged from intensive care after severe COVID-19: a mixed-methods study of the views of UK general practitioners and intensive care staff during the pandemic’s first wave
This article has 4 authors:Reviewed by ScreenIT
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Markers of Polyfunctional SARS-CoV-2 Antibodies in Convalescent Plasma
This article has 22 authors:Reviewed by ScreenIT
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ARIMA modelling of predicting COVID-19 infections
This article has 2 authors:Reviewed by ScreenIT
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The Effect of Neutropenia and Filgrastim (G-CSF) on Cancer Patients With Coronavirus Disease 2019 (COVID-19) Infection
This article has 13 authors:Reviewed by ScreenIT
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New approximations, and policy implications, from a delayed dynamic model of a fast pandemic
This article has 2 authors:Reviewed by ScreenIT
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Household secondary attack rate of COVID-19 by household size and index case characteristics
This article has 5 authors:Reviewed by ScreenIT
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Combined in silico and in vitro approaches identified the antipsychotic drug lurasidone and the antiviral drug elbasvir as SARS-CoV2 and HCoV-OC43 inhibitors
This article has 9 authors:Reviewed by ScreenIT
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Effect of Temperature on the Transmission of COVID-19: A Machine Learning Case Study in Spain
This article has 2 authors:Reviewed by ScreenIT
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Age-specific mortality and immunity patterns of SARS-CoV-2
This article has 9 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT