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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Hydroxychloroquine for the treatment of severe respiratory infection by COVID-19: A randomized controlled trial
This article has 8 authors:Reviewed by ScreenIT
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Clinical validation of a multiplex PCR-based detection assay using saliva or nasopharyngeal samples for SARS-Cov-2, influenza A and B
This article has 15 authors:Reviewed by ScreenIT
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Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study
This article has 23 authors:Reviewed by ScreenIT
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Estimation and Monitoring of COVID-19's Transmissibility From Publicly Available Data
This article has 2 authors:Reviewed by ScreenIT
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A Projection Model of COVID-19 Pandemic for Belgium
This article has 3 authors:Reviewed by ScreenIT
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Performance assessment of 11 commercial serological tests for SARS-CoV-2 on hospitalised COVID-19 patients
This article has 22 authors:Reviewed by ScreenIT
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A SARS-CoV-2 Reference Standard Quantified by Multiple Digital PCR Platforms for Quality Assessment of Molecular Tests
This article has 8 authors:Reviewed by ScreenIT
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A COVID-19 infection risk model for frontline health care workers
This article has 2 authors:Reviewed by ScreenIT
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A Computational Approach to Evaluate the Combined Effect of SARS-CoV-2 RBD Mutations and ACE2 Receptor Genetic Variants on Infectivity: The COVID-19 Host-Pathogen Nexus
This article has 6 authors:Reviewed by ScreenIT
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Effect modification of the association between comorbidities and severe course of COVID-19 disease by age of study participants: a systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT