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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Lessons from a rapid systematic review of early SARS-CoV-2 serosurveys
This article has 8 authors:Reviewed by ScreenIT
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Patterns of COVID-19 related excess mortality in the municipalities of Northern Italy during the first wave of the pandemic
This article has 8 authors:Reviewed by ScreenIT
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Early estimation of the risk factors for hospitalization and mortality by COVID-19 in Mexico
This article has 5 authors:Reviewed by ScreenIT
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Monitoring and Tracking the Evolution of a Viral Epidemic Through Nonlinear Kalman Filtering: Application to the COVID-19 Case
This article has 3 authors:Reviewed by ScreenIT
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Rapid implementation of SARS-CoV-2 sequencing to investigate cases of health-care associated COVID-19: a prospective genomic surveillance study
This article has 28 authors:Reviewed by ScreenIT
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Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis
This article has 5 authors:Reviewed by ScreenIT
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Deep Transfer Learning-Based COVID-19 Prediction Using Chest X-Rays
This article has 3 authors:Reviewed by ScreenIT
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Machine learning model estimating number of COVID-19 infection cases over coming 24 days in every province of South Korea (XGBoost and MultiOutputRegressor)
This article has 5 authors:Reviewed by ScreenIT
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Cross-sectional IgM and IgG profiles in SARS-CoV-2 infection
This article has 13 authors:Reviewed by ScreenIT
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Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve
This article has 3 authors:Reviewed by ScreenIT