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 GLasses Against transmission of SARS-CoV-2 in the communitY (GLASSY) trial: A pragmatic randomized trial (study protocol)
This article has 6 authors:Reviewed by ScreenIT
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Using Digital Humanities for Understanding COVID-19: Lessons from Digital History about Earlier Coronavirus Pandemic
This article has 1 author:Reviewed by ScreenIT
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Dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 in England
This article has 22 authors:Reviewed by ScreenIT
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Interactions among 17 respiratory pathogens: a cross-sectional study using clinical and community surveillance data
This article has 35 authors:Reviewed by ScreenIT
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Minimizing school disruption under high incidence conditions due to the Omicron variant in early 2022
This article has 7 authors:Reviewed by ScreenIT
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SARS-CoV-2 testing strategies for outbreak mitigation in vaccinated populations
This article has 5 authors:Reviewed by ScreenIT
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ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves
This article has 6 authors:Reviewed by ScreenIT
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Effects of socio-economic factors on elementary school student COVID-19 infections in Ontario, Canada
This article has 4 authors:Reviewed by ScreenIT
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Emulation of a Target Trial From Observational Data to Compare Effectiveness of Casirivimab/Imdevimab and Bamlanivimab/Etesevimab for Early Treatment of Non-Hospitalized Patients With COVID-19
This article has 21 authors:Reviewed by ScreenIT
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Comparative effectiveness of different primary vaccination courses on mRNA-based booster vaccines against SARs-COV-2 infections: a time-varying cohort analysis using trial emulation in the Virus Watch community cohort
This article has 17 authors:Reviewed by ScreenIT