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 impact of vital signs on the death of patients with new coronavirus pneumonia: A systematic review and meta-analysis
This article has 5 authors:Reviewed by ScreenIT
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Readability of selected governmental and popular health organization websites on Covid-19 public health information: A descriptive analysis
This article has 4 authors:Reviewed by ScreenIT
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The CXCR6/CXCL16 axis links inflamm-aging to disease severity in COVID-19 patients
This article has 16 authors:Reviewed by ScreenIT
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LUNG ULTRASOUND FINDINGS IN PATIENTS WITH COVID-19 IN AN URBAN EMERGENCY DEPARTMENT IN THE UK – AN OBSERVATIONAL STUDY
This article has 6 authors:Reviewed by ScreenIT
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A comprehensive library of fluorescent constructs of SARS‐CoV‐2 proteins and their initial characterisation in different cell types
This article has 14 authors:Reviewed by ScreenIT
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Galectin antagonist use in mild cases of SARS-CoV-2; pilot feasibility randomised, open label, controlled trial
This article has 9 authors:Reviewed by ScreenIT
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Evolutionary analysis of SARS-CoV-2 spike protein for its different clades
This article has 6 authors:Reviewed by ScreenIT
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Hesitant or not? A global survey of potential acceptance of a COVID-19 vaccine
This article has 8 authors:Reviewed by ScreenIT
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Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity
This article has 14 authors:Reviewed by ScreenIT
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COVID-19 Among People Living with HIV: A Systematic Review
This article has 4 authors:Reviewed by ScreenIT