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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Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19
This article has 25 authors:Reviewed by ScreenIT
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Restoration of leukomonocyte counts is associated with viral clearance in COVID-19 hospitalized patients
This article has 24 authors:Reviewed by ScreenIT
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Case Fatality Risk of the First Pandemic Wave of Coronavirus Disease 2019 (COVID-19) in China
This article has 22 authors:Reviewed by ScreenIT
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Modelling-based evaluation of the effect of quarantine control by the Chinese government in the coronavirus disease 2019 outbreak
This article has 9 authors:Reviewed by ScreenIT
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Clinical Findings of COVID-19 Patients Admitted to Intensive Care Units in Guangdong Province, China: A Multicenter, Retrospective, Observational Study
This article has 19 authors:Reviewed by ScreenIT
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Immunodepletion with Hypoxemia: A Potential High Risk Subtype of Coronavirus Disease 2019
This article has 13 authors:Reviewed by ScreenIT
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Clinical features and short-term outcomes of 221 patients with COVID-19 in Wuhan, China
This article has 8 authors:Reviewed by ScreenIT
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Clinical and Laboratory Profiles of 75 Hospitalized Patients with Novel Coronavirus Disease 2019 in Hefei, China
This article has 19 authors:Reviewed by ScreenIT
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The role of interleukin‐6 in monitoring severe case of coronavirus disease 2019
This article has 13 authors:Reviewed by ScreenIT
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Psychological impact of the coronavirus disease 2019 (COVID-19) outbreak on healthcare workers in China
This article has 5 authors:Reviewed by ScreenIT