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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Collateral damage: the impact on outcomes from cancer surgery of the COVID-19 pandemic
This article has 23 authors:Reviewed by ScreenIT
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Risk Assessment of Drug‐Induced Long QT Syndrome for Some COVID‐19 Repurposed Drugs
This article has 7 authors:Reviewed by ScreenIT
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Current Understanding of COVID-19 Clinical Course and Investigational Treatments
This article has 12 authors:Reviewed by ScreenIT
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A web-based Diagnostic Tool for COVID-19 Using Machine Learning on Chest Radiographs (CXR)
This article has 4 authors:Reviewed by ScreenIT
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CT in relation to RT-PCR in diagnosing COVID-19 in The Netherlands: A prospective study
This article has 9 authors:Reviewed by ScreenIT
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Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing
This article has 2 authors:Reviewed by ScreenIT
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A follow-up study of children infected with SARS-CoV-2 from western China
This article has 42 authors:Reviewed by ScreenIT
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A cohort study of 223 patients explores the clinical risk factors for the severity diagnosis of COVID-19
This article has 8 authors:Reviewed by ScreenIT
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No Clear Benefit to the Use of Corticosteroid as Treatment in Adult Patients with Coronavirus Disease 2019 : A Retrospective Cohort Study
This article has 9 authors:Reviewed by ScreenIT
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Clinical and Imaging Findings in COVID-19 Patients Complicated by Pulmonary Embolism
This article has 6 authors:Reviewed by ScreenIT