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 and frontline health workers in West Africa: a scoping review
This article has 5 authors:Reviewed by ScreenIT
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Closed environments facilitate secondary transmission of coronavirus disease 2019 (COVID-19)
This article has 9 authors:Reviewed by ScreenIT
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The impact of population-wide rapid antigen testing on SARS-CoV-2 prevalence in Slovakia
This article has 11 authors:Reviewed by ScreenIT
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Impact of the COVID-19 pandemic and related control measures on cancer diagnosis in Catalonia: a time-series analysis of primary care electronic health records covering about five million people
This article has 10 authors:Reviewed by ScreenIT
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A high-throughput microsphere-based immunoassay of anti-SARS-CoV-2 IgM testing for COVID-19 diagnostics
This article has 6 authors:Reviewed by ScreenIT
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Rapid development of a de novo convalescent plasma program in response to a global pandemic: A large southeastern U.S. blood center's experience
This article has 12 authors:Reviewed by ScreenIT
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Impact of social restrictions during the COVID-19 pandemic on the physical activity levels of adults aged 50–92 years: a baseline survey of the CHARIOT COVID-19 Rapid Response prospective cohort study
This article has 10 authors:Reviewed by ScreenIT
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Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients
This article has 48 authors:Reviewed by ScreenIT
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Establishment of a reverse genetics system for SARS-CoV-2 using circular polymerase extension reaction
This article has 10 authors:Reviewed by ScreenIT
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The use of procalcitonin as an antimicrobial stewardship tool and a predictor of disease severity in coronavirus disease 2019 (COVID-19)
This article has 4 authors:Reviewed by ScreenIT