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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A Model for SARS-CoV-2 Infection with Treatment
This article has 2 authors:Reviewed by ScreenIT
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Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study
This article has 48 authors:Reviewed by ScreenIT
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Variation of quantified infection rates of mixed samples to enhance rapid testing during an epidemic
This article has 1 author:Reviewed by ScreenIT
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The values of coagulation function in COVID-19 patients
This article has 9 authors:Reviewed by ScreenIT
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A nationwide survey of UK cardiac surgeons' view on clinical decision making during the coronavirus disease 2019 (COVID-19) pandemic
This article has 5 authors:Reviewed by ScreenIT
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Extensions of the SEIR model for the analysis of tailored social distancing and tracing approaches to cope with COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Contextualizing COVID-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave
This article has 3 authors:Reviewed by ScreenIT
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Informed sequential pooling approach to detect SARS-CoV-2 infection
This article has 2 authors:Reviewed by ScreenIT
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COVID-19 in South Africa: outbreak despite interventions
This article has 7 authors:Reviewed by ScreenIT
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Variation of National and International Guidelines on Respiratory Protection for Health Care Professionals During the COVID-19 Pandemic
This article has 7 authors:Reviewed by ScreenIT