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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Evaluating Population Density as a Parameter for Optimizing COVID-19 Testing: Statistical Analysis
This article has 3 authors:Reviewed by ScreenIT
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Genetic variants are identified to increase risk of COVID-19 related mortality from UK Biobank data
This article has 5 authors:Reviewed by ScreenIT
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Early prediction of mortality risk among patients with severe COVID-19, using machine learning
This article has 23 authors:Reviewed by ScreenIT
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Characterising long COVID: a living systematic review
This article has 20 authors:Reviewed by ScreenIT
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Acceptable performance of the Abbott ID NOW among symptomatic individuals with confirmed COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Clinical Evaluation of a COVID-19 Antibody Lateral Flow Assay using Point of Care Samples
This article has 10 authors:Reviewed by ScreenIT
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SARS-CoV-2 in river water: Implications in low sanitation countries
This article has 6 authors:Reviewed by ScreenIT
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CovidSIMVL – Agent-Based Modeling of Localized Transmission within a Heterogeneous Array of Locations – Motivation, Configuration and Calibration
This article has 2 authors:Reviewed by ScreenIT
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A new model to prioritize waiting lists for elective surgery under the COVID-19 pandemic pressure
This article has 30 authors:Reviewed by ScreenIT
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Persistent viral RNA shedding after COVID ‐19 symptom resolution in older convalescent plasma donors
This article has 3 authors:Reviewed by ScreenIT