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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A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors
This article has 3 authors:Reviewed by ScreenIT
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Cohort profile: Actionable Register of Geneva Outpatients and inpatients with SARS-CoV-2 (ARGOS)
This article has 16 authors:Reviewed by ScreenIT
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Brain injury, endothelial injury and inflammatory markers are elevated and express sex-specific alterations after COVID-19
This article has 16 authors:Reviewed by ScreenIT
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Effectiveness of BNT162b2 and mRNA-1273 covid-19 vaccines against symptomatic SARS-CoV-2 infection and severe covid-19 outcomes in Ontario, Canada: test negative design study
This article has 22 authors:Reviewed by ScreenIT
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Recovery from the COVID-19 pandemic by mass vaccination: emergent lessons from the United States and India
This article has 4 authors:Reviewed by ScreenIT
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Inhibition of SARS-CoV-2 Infection by Human Defensin HNP1 and Retrocyclin RC-101
This article has 7 authors:Reviewed by ScreenIT
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In-hospital cardiac arrest in Intensive Care Unit versus non-Intensive Care Unit patients with COVID-19. A systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT
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Estimating the wave 1 and wave 2 infection fatality rates from SARS-CoV-2 in India
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 Nsp14 activates NF-κB signaling and induces IL-8 upregulation
This article has 10 authors:Reviewed by ScreenIT
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An issue of concern: unique truncated ORF8 protein variants of SARS-CoV-2
This article has 24 authors: