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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Coronavirus Disease 2019 as Cause of Viral Sepsis: A Systematic Review and Meta-Analysis*
This article has 8 authors:Reviewed by ScreenIT
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Community prevalence of SARS-CoV-2 virus in England during May 2020: REACT study
This article has 16 authors:Reviewed by ScreenIT
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Estimating the false-negative test probability of SARS-CoV-2 by RT-PCR
This article has 4 authors:Reviewed by ScreenIT
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A single dose, BCG-adjuvanted COVID-19 vaccine provides sterilizing immunity against SARS-CoV-2 infection in mice
This article has 23 authors:Reviewed by ScreenIT
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Neuraminidase inhibitors rewire neutrophil function in vivo in murine sepsis and ex vivo in COVID-19
This article has 37 authors:Reviewed by ScreenIT
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Herd immunity thresholds for SARS-CoV-2 estimated from unfolding epidemics
This article has 4 authors:Reviewed by ScreenIT, Rapid Reviews Infectious Diseases
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Adoption, feasibility and safety of a family medicine–led remote monitoring program for patients with COVID-19: a descriptive study
This article has 11 authors:Reviewed by ScreenIT
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Viral mutation, contact rates and testing: a DCM study of fluctuations
This article has 4 authors:Reviewed by ScreenIT
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Assessment of COVID-19 Pandemic in Nepal: A Lockdown Scenario Analysis
This article has 3 authors:Reviewed by ScreenIT
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Evaluation of the IgG antibody response to SARS CoV-2 infection and performance of a lateral flow immunoassay: cross-sectional and longitudinal analysis over 11 months
This article has 23 authors:Reviewed by ScreenIT