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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Prioritisation by FIT to mitigate the impact of delays in the 2-week wait colorectal cancer referral pathway during the COVID-19 pandemic: a UK modelling study
This article has 22 authors:Reviewed by ScreenIT
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Dynamics of an SEIRS COVID-19 Epidemic Model with Saturated Incidence and Saturated Treatment Response: Bifurcation Analysis and Simulations
This article has 8 authors:Reviewed by ScreenIT
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Strategies to target SARS-CoV-2 entry and infection using dual mechanisms of inhibition by acidification inhibitors
This article has 26 authors:Reviewed by ScreenIT
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Managing the risk of a COVID-19 outbreak from border arrivals
This article has 6 authors:Reviewed by ScreenIT
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Covid-19 and dysregulated cerebral perfusion: observations with multimodal MRI
This article has 12 authors:Reviewed by ScreenIT
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Indoor Dust as a Matrix for Surveillance of COVID-19
This article has 11 authors:Reviewed by ScreenIT
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Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases
This article has 8 authors:Reviewed by ScreenIT
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Disruptions to schistosomiasis programmes due to COVID-19: an analysis of potential impact and mitigation strategies
This article has 5 authors:Reviewed by ScreenIT
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“Return to University Campuses Associated with 9% Increase in New COVID-19 Case Rate”
This article has 3 authors: -
HOW IS THE IMPACT ON PUBLIC HEALTH OF SECOND WAVE OF COVID-19 PANDEMIC COMPARED TO THE FIRST WAVE? CASE STUDY OF ITALY
This article has 1 author:Reviewed by ScreenIT