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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Associations between state-level healthcare access and COVID-19 case trajectories in the United States
This article has 4 authors:Reviewed by ScreenIT
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Randomized controlled trials of remdesivir in hospitalized coronavirus disease 2019 patients
This article has 9 authors:Reviewed by ScreenIT
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Anticardiolipin and other antiphospholipid antibodies in critically ill COVID-19 positive and negative patients
This article has 6 authors:Reviewed by ScreenIT
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Low-income neighbourhood was a key determinant of severe COVID-19 incidence during the first wave of the epidemic in Paris
This article has 6 authors:Reviewed by ScreenIT
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Comparing COVID-19 and Influenza Presentation and Trajectory
This article has 8 authors:Reviewed by ScreenIT
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Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves
This article has 1 author:Reviewed by ScreenIT
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High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County
This article has 7 authors:Reviewed by ScreenIT
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Multicenter evaluation of the Panbio™ COVID-19 rapid antigen-detection test for the diagnosis of SARS-CoV-2 infection
This article has 32 authors:Reviewed by ScreenIT
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Are college campuses superspreaders? A data-driven modeling study
This article has 6 authors:Reviewed by ScreenIT
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Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil
This article has 23 authors:Reviewed by ScreenIT