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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Treatment provision for adults with ADHD during the COVID-19 pandemic: an exploratory study on patient and therapist experience with on-site sessions using face masks vs. telepsychiatric sessions
This article has 7 authors:Reviewed by ScreenIT
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Risk of hospital admission with coronavirus disease 2019 in healthcare workers and their households: nationwide linkage cohort study
This article has 17 authors:Reviewed by ScreenIT
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Spread of the Novel Coronavirus (SARS-CoV-2): Modeling and Simulation of Control Strategies
This article has 1 author:Reviewed by ScreenIT
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Critical levels of mask efficiency and of mask adoption that theoretically extinguish respiratory virus epidemics
This article has 1 author:Reviewed by ScreenIT
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Estimation of the Probability of Reinfection With COVID-19 by the Susceptible-Exposed-Infectious-Removed-Undetectable-Susceptible Model
This article has 1 author:Reviewed by ScreenIT
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Socio-demographic, clinical, hospital admission and oxygen requirement characteristics of COVID-19 patients of Bangladesh
This article has 8 authors:Reviewed by ScreenIT
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Therapeutic Effectiveness of Interferon Alpha 2b Treatment for COVID-19 Patient Recovery
This article has 11 authors:Reviewed by ScreenIT
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SARS-CoV-2 Seroprevalence Survey Estimates Are Affected by Anti-Nucleocapsid Antibody Decline
This article has 8 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Containment measures limit environmental effects on COVID-19 early outbreak dynamics
This article has 2 authors:Reviewed by ScreenIT
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Investigating the origin of the Belgian second SARS-CoV-2 wave by using (pre)admission screening samples
This article has 8 authors:Reviewed by ScreenIT