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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Are high urea values before intravenous immunoglobulin replacement a risk factor for COVID-related mortality?
This article has 2 authors:Reviewed by ScreenIT
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Assessment of Out of Pocket Expenditure and associated factors for availing COVID-19 vaccination by the beneficiaries in Bengaluru: South India
This article has 5 authors:Reviewed by ScreenIT
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Doubtful clinical benefit of casirivimab-imdevimab treatment for disease severity outcome of high-risk patients with SARS-CoV-2 delta variant infection
This article has 6 authors:Reviewed by ScreenIT
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Breakthrough SARS-CoV-2 infections in MS patients on disease-modifying therapies
This article has 29 authors:Reviewed by ScreenIT
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An ML prediction model based on clinical parameters and automated CT scan features for COVID-19 patients
This article has 5 authors:Reviewed by ScreenIT
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Viral Cultures for Assessing Airborne Transmission of SARs-CoV-2: a Systematic Review Protocol (version 1)
This article has 10 authors:Reviewed by ScreenIT
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Dynamics of antibody titers and cellular immunity among Japanese healthcare workers during the 6 months after receiving two doses of BNT162b2 mRNA vaccine
This article has 18 authors:Reviewed by ScreenIT
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Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging
This article has 14 authors:Reviewed by ScreenIT
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Serial Intervals for SARS-CoV-2 Omicron and Delta Variants, Belgium, November 19–December 31, 2021
This article has 6 authors:Reviewed by ScreenIT
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Predicting clinical outcomes and hospitalization stay of hospitalized COVID-19 patients by using Deep Learning methods
This article has 1 author:Reviewed by ScreenIT