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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ACP risk grade: a simple mortality index for patients with confirmed or suspected severe acute respiratory syndrome coronavirus 2 disease (COVID-19) during the early stage of outbreak in Wuhan, China
This article has 22 authors:Reviewed by ScreenIT
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Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19
This article has 13 authors:Reviewed by ScreenIT
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Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China
This article has 14 authors:Reviewed by ScreenIT
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SARS-CoV-2 infection does not significantly cause acute renal injury: an analysis of 116 hospitalized patients with COVID-19 in a single hospital, Wuhan, China
This article has 7 authors:Reviewed by ScreenIT
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Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China
This article has 5 authors:Reviewed by ScreenIT
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Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics
This article has 26 authors:Reviewed by ScreenIT
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Vulnerabilities in coronavirus glycan shields despite extensive glycosylation
This article has 10 authors:Reviewed by ScreenIT
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Molecular Mechanism of Evolution and Human Infection with SARS-CoV-2
This article has 5 authors:Reviewed by ScreenIT
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The role of institutional trust in preventive practices and treatment-seeking intention during the coronavirus disease 2019 outbreak among residents in Hubei, China
This article has 11 authors:Reviewed by ScreenIT
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Rapid reconstruction of SARS-CoV-2 using a synthetic genomics platform
This article has 26 authors:Reviewed by ScreenIT