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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The U-shaped association of serum iron level with disease severity in adult hospitalized patients with COVID-19
This article has 13 authors:Reviewed by ScreenIT
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Interrogating structural inequalities in COVID-19 mortality in England and Wales
This article has 5 authors:Reviewed by ScreenIT
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Super-spreading events initiated the exponential growth phase of COVID-19 with ℛ 0 higher than initially estimated
This article has 3 authors:Reviewed by ScreenIT
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Clinical effectiveness of drugs in hospitalized patients with COVID-19: a systematic review and meta-analysis
This article has 4 authors:Reviewed by ScreenIT
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Unbuttoning the impact of N501Y mutant RBD on viral entry mechanism: A computational insight
This article has 5 authors:Reviewed by ScreenIT
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Combined RT-qPCR and pyrosequencing of a Spike glycoprotein polybasic cleavage motif can uncover pediatric SARS-CoV-2 infections associated with heterogeneous presentation
This article has 13 authors:Reviewed by ScreenIT
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An expert judgment model to predict early stages of the COVID-19 pandemic in the United States
This article has 2 authors:Reviewed by ScreenIT
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COVIDTrach: a prospective cohort study of mechanically ventilated patients with COVID-19 undergoing tracheostomy in the UK
This article has 1 author:Reviewed by ScreenIT
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Identifying Synergistic Interventions to Address COVID-19 Using a Large Scale Agent-Based Model
This article has 2 authors:Reviewed by ScreenIT
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Factors influencing the COVID-19 daily deaths' peak across European countries
This article has 3 authors:Reviewed by ScreenIT