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 and Population Age Structure
This article has 2 authors:Reviewed by ScreenIT
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The Role of Disease Severity and Demographics in the Clinical Course of COVID-19 Patients Treated With Convalescent Plasma
This article has 17 authors:Reviewed by ScreenIT
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Social Behaviors Associated With a Positive COVID-19 Test Result
This article has 8 authors:Reviewed by ScreenIT
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Prediction of Mortality in hospitalized COVID-19 patients in a statewide health network
This article has 9 authors:Reviewed by ScreenIT
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Alternate primers for whole-genome SARS-CoV-2 sequencing
This article has 4 authors:Reviewed by ScreenIT
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Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review
This article has 8 authors:Reviewed by ScreenIT
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Antibodies that potently inhibit or enhance SARS-CoV-2 spike protein-ACE2 interaction isolated from synthetic single-chain antibody libraries
This article has 14 authors:Reviewed by ScreenIT
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Impact of mass testing during an epidemic rebound of SARS-CoV-2: a modelling study using the example of France
This article has 7 authors:Reviewed by ScreenIT
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Prevalence of antibodies to SARS-CoV-2 in healthy blood donors in New York
This article has 12 authors:Reviewed by ScreenIT
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CCOFEE-GI Study: Colombian COVID19 First Experience in Gastroentrology. Characterization of digestive manifestations in patients diagnosed with COVID-19 at a highly complex institution in Bogota D.C., Colombia
This article has 11 authors:Reviewed by ScreenIT