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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Identification of spatial variations in COVID-19 epidemiological data using K-Means clustering algorithm: a global perspective
This article has 1 author:Reviewed by ScreenIT
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Implementation and evaluation of a novel real-time multiplex assay for SARS-CoV-2: in-field learnings from a clinical microbiology laboratory
This article has 13 authors:Reviewed by ScreenIT
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iSCAN: An RT-LAMP-coupled CRISPR-Cas12 module for rapid, sensitive detection of SARS-CoV-2
This article has 19 authors:Reviewed by ScreenIT
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Estimating excess visual loss from neovascular age-related macular degeneration in the UK during the COVID-19 pandemic: a retrospective clinical audit and simulation model
This article has 14 authors:Reviewed by ScreenIT
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Psychological Effects of COVID-19 Among Health Care Workers, and How They Are Coping: A Web-Based, Cross-Sectional Study During the First Wave of COVID-19 in Pakistan
This article has 12 authors:Reviewed by ScreenIT
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A Fractal Viewpoint to COVID-19 Infection
This article has 3 authors:Reviewed by ScreenIT
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A Model for the Testing and Tracing Needed to Suppress COVID-19
This article has 1 author:Reviewed by ScreenIT
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Clinical management and mortality among COVID-19 cases in sub-Saharan Africa: A retrospective study from Burkina Faso and simulated case analysis
This article has 14 authors:Reviewed by ScreenIT
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ACE2 levels are altered in comorbidities linked to severe outcome in COVID-19
This article has 10 authors:Reviewed by ScreenIT
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Retraction notice for: “Characteristics and risk factors for COVID-19 diagnosis and adverse outcomes in Mexico: an analysis of 89,756 laboratory-confirmed COVID-19 cases.” Theodoros V. Giannouchos, Roberto A. Sussman, José M. Mier, Konstantinos Poulas and Konstantinos Farsalinos. Eur Respir J 2020; in press.
Reviewed by ScreenIT