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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The role of corticosteroids in the management of critically ill patients with coronavirus disease 2019 (COVID-19): A meta-analysis
This article has 4 authors:Reviewed by ScreenIT
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An 81-Nucleotide Deletion in SARS-CoV-2 ORF7a Identified from Sentinel Surveillance in Arizona (January to March 2020)
This article has 10 authors:Reviewed by ScreenIT
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COVID-19 Outbreak Prediction with Machine Learning
This article has 8 authors:Reviewed by ScreenIT
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Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing
This article has 12 authors:Reviewed by ScreenIT
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Persistent viral shedding of SARS‐CoV‐2 in faeces – a rapid review
This article has 5 authors:Reviewed by ScreenIT
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Mental Health Outcomes Among Frontline and Second-Line Health Care Workers During the Coronavirus Disease 2019 (COVID-19) Pandemic in Italy
This article has 7 authors:Reviewed by ScreenIT
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Review of Current Evidence of Hydroxychloroquine in Pharmacotherapy of COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Estimates of COVID-19 case-fatality risk from individual-level data
This article has 3 authors:Reviewed by ScreenIT
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Effect of blood analysis and immune function on the prognosis of patients with COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Effectiveness of intravenous immunoglobulin for children with severe COVID-19: a rapid review
This article has 18 authors:Reviewed by ScreenIT