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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REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY
This article has 3 authors:Reviewed by ScreenIT
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Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave
This article has 12 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Face masks effectively limit the probability of SARS-CoV-2 transmission
This article has 8 authors:Reviewed by ScreenIT
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Increased Peripheral Blood Neutrophil Activation Phenotypes and Neutrophil Extracellular Trap Formation in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients: A Case Series and Review of the Literature
This article has 34 authors:Reviewed by ScreenIT
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Comprehensive analysis of genomic diversity of SARS-CoV-2 in different geographic regions of India: An endeavour to classify Indian SARS-CoV-2 strains on the basis of co-existing mutations
This article has 7 authors:Reviewed by ScreenIT
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A systematic review of the associations between care home ownership and COVID-19 outbreaks, infections and mortality
This article has 4 authors:Reviewed by ScreenIT
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The Build-Up of Droplet/Aerosols Carrying the SARS-CoV-2 Coronavirus, in Confined Spaces
This article has 2 authors:Reviewed by ScreenIT
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Longitudinal Monitoring of SARS-CoV-2 RNA on High-Touch Surfaces in a Community Setting
This article has 9 authors:Reviewed by ScreenIT, Rapid Reviews Infectious Diseases
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Bacterial pulmonary superinfections are associated with longer duration of ventilation in critically ill COVID-19 patients
This article has 14 authors:Reviewed by ScreenIT
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Epitope profiling reveals binding signatures of SARS-CoV-2 immune response in natural infection and cross-reactivity with endemic human CoVs
This article has 14 authors:Reviewed by ScreenIT