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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Dynamic Change and Clinical Relevance of Postinfectious SARS-CoV-2 Antibody Responses
This article has 27 authors:Reviewed by ScreenIT
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Problems with evidence assessment in COVID-19 health policy impact evaluation: a systematic review of study design and evidence strength
This article has 24 authors:Reviewed by ScreenIT
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Estimation of Case Fatality Rate during an Epidemic: an Example from COVID-19 Pandemic
This article has 3 authors:Reviewed by ScreenIT
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Assessing the effects of non-pharmaceutical interventions on SARS-CoV-2 transmission in Belgium by means of an extended SEIQRD model and public mobility data
This article has 7 authors:Reviewed by ScreenIT
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Susceptibility-adjusted herd immunity threshold model and potential R 0 distribution fitting the observed Covid-19 data in Stockholm
This article has 2 authors:Reviewed by ScreenIT
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A Systematic Review of the Cardiovascular Manifestations and Outcomes in the Setting of Coronavirus-19 Disease
This article has 15 authors:Reviewed by ScreenIT
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Evaluation of Group Testing for SARS-CoV-2 RNA
This article has 3 authors:Reviewed by ScreenIT
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PACIFIC: A lightweight deep-learning classifier of SARS-CoV-2 and co-infecting RNA viruses
This article has 5 authors:Reviewed by ScreenIT
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Safety and Immunogenicity of Two RNA-Based Covid-19 Vaccine Candidates
This article has 25 authors:Reviewed by ScreenIT
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Association Between Nursing Home Crowding and COVID-19 Infection and Mortality in Ontario, Canada
This article has 8 authors:Reviewed by ScreenIT