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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Personalized Virus Load Curves of SARS-CoV-2 Infection
This article has 3 authors:Reviewed by ScreenIT
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Methodological quality of COVID-19 clinical research
This article has 11 authors:Reviewed by ScreenIT
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On COVID-19-safety ranking of seats in intercontinental commercial aircrafts: A preliminary multiphysics computational perspective
This article has 3 authors:Reviewed by ScreenIT
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Serological prevalence of SARS-CoV-2 infection and associated factors in healthcare workers in a “non-COVID” hospital in Mexico City
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 Seroprevalence among Healthcare Workers of a Large COVID-19 Hospital in Rome Reveals Strengths and Limits of Two Different Serological Tests
This article has 27 authors:Reviewed by ScreenIT
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S gene dropout patterns in SARS-CoV-2 tests suggest spread of the H69del/V70del mutation in the US
This article has 6 authors:Reviewed by ScreenIT
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On the Effects of Misclassification in Estimating Efficacy With Application to Recent COVID-19 Vaccine Trials
This article has 1 author:Reviewed by ScreenIT
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Effects of Cocooning on Coronavirus Disease Rates after Relaxing Social Distancing
This article has 9 authors:Reviewed by ScreenIT
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Aprepitant as a combinant with Dexamethasone reduces the inflammation via Neurokinin 1 Receptor Antagonism in severe to critical Covid-19 patients and potentiates respiratory recovery: A novel therapeutic approach
This article has 7 authors:Reviewed by ScreenIT
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Improved COVID-19 testing by extraction free SARS-Cov-2 RT-PCR
This article has 1 author:Reviewed by ScreenIT