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 impact of host resistance on cumulative mortality and the threshold of herd immunity for SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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High SARS-CoV-2 seroprevalence in health care workers but relatively low numbers of deaths in urban Malawi
This article has 23 authors:Reviewed by ScreenIT
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Real-life clinical sensitivity of SARS-CoV-2 RT-PCR test in symptomatic patients
This article has 21 authors:Reviewed by ScreenIT
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State social distancing restrictions and nursing home outcomes
This article has 5 authors:Reviewed by ScreenIT
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Perceived Interruptions to HIV Prevention and Treatment Services Associated With COVID-19 for Gay, Bisexual, and Other Men Who Have Sex With Men in 20 Countries
This article has 12 authors:Reviewed by ScreenIT
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Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review
This article has 11 authors:Reviewed by ScreenIT
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Mandatory Social Distancing Associated With Increased Doubling Time: An Example Using Hyperlocal Data
This article has 2 authors:Reviewed by ScreenIT
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Development of Electrospun Nanofibrous Filters for Controlling Coronavirus Aerosols
This article has 8 authors:Reviewed by ScreenIT
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Knowledge, attitudes and practices associated with the COVID-19 among slum dwellers resided in Dhaka City: a Bangladeshi interview-based survey
This article has 7 authors:Reviewed by ScreenIT
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Asymptomatic Employee Screening for SARS-CoV-2: Implementation of and Reactions to an Employer-Based Testing Program
This article has 7 authors:Reviewed by ScreenIT