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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Impact of COVID-19 pandemic on severity of illness and resources required during intensive care in the greater New York City area
This article has 6 authors:Reviewed by ScreenIT
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Preliminary study to identify severe from moderate cases of COVID-19 using combined hematology parameters
This article has 13 authors:Reviewed by ScreenIT
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Optimal Control applied to a SEIR model of 2019-nCoV with social distancing
This article has 1 author:Reviewed by ScreenIT
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Impact of Social Vulnerability on COVID-19 Incidence and Outcomes in the United States
This article has 11 authors:Reviewed by ScreenIT
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A novel specific artificial intelligence-based method to identify COVID-19 cases using simple blood exams
This article has 1 author:Reviewed by ScreenIT
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Cost–benefit of limited isolation and testing in COVID-19 mitigation
This article has 2 authors:Reviewed by ScreenIT
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Risk Assesment of nCOVID-19 Pandemic In India: A Mathematical Model And Simulation
This article has 2 authors:Reviewed by ScreenIT
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Calcium channel blocker amlodipine besylate therapy is associated with reduced case fatality rate of COVID-19 patients with hypertension
This article has 16 authors:Reviewed by ScreenIT
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Clinical efficacy of hydroxychloroquine in patients with covid-19 pneumonia who require oxygen: observational comparative study using routine care data
This article has 34 authors:Reviewed by ScreenIT
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Using random testing in a feedback-control loop to manage a safe exit from the COVID-19 lockdown
This article has 4 authors:Reviewed by ScreenIT