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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Effectiveness of interventions to reduce COVID-19 transmission in a large urban jail: a model-based analysis
This article has 5 authors:Reviewed by ScreenIT
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JAK-inhibitor and type I interferon ability to produce favorable clinical outcomes in COVID-19 patients: a systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 antigen rapid diagnostic test enhanced with silver amplification technology
This article has 17 authors:Reviewed by ScreenIT
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SARS-CoV-2 PCR cycle threshold at hospital admission associated with patient mortality
This article has 9 authors:Reviewed by ScreenIT
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Modeling Covid-19 dynamics for real-time estimates and projections: an application to Albanian data
This article has 1 author:Reviewed by ScreenIT
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Saliva as a testing sample for SARS-CoV-2 detection by RT-PCR in low prevalence community settings
This article has 10 authors:Reviewed by ScreenIT
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Flattening the COVID 19 curve in susceptible forest indigenous tribes using SIR model
This article has 1 author:Reviewed by ScreenIT
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Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data
This article has 4 authors:Reviewed by ScreenIT
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Development of a data-driven COVID-19 prognostication tool to inform triage and step-down care for hospitalised patients in Hong Kong: a population-based cohort study
This article has 9 authors:Reviewed by ScreenIT
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Discovery of TMPRSS2 inhibitors from virtual screening
This article has 12 authors:Reviewed by ScreenIT