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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Case–control study of the association of chronic acid suppression and social determinants of health with COVID-19 infection
This article has 12 authors:Reviewed by ScreenIT
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Using Non-Pharmaceutical Interventions and High Isolation of Asymptomatic Carriers to Contain the Spread of SARS-CoV-2 in Nursing Homes
This article has 5 authors:Reviewed by ScreenIT
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Quantitative UV-C dose validation with photochromic indicators for informed N95 emergency decontamination
This article has 5 authors:Reviewed by ScreenIT
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Spatial Allocation of Scarce COVID-19 Vaccines *†‡
This article has 6 authors:Reviewed by ScreenIT
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An interpretable mortality prediction model for COVID-19 patients – alternative approach
This article has 1 author:Reviewed by ScreenIT
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Validating the RISE UP score for predicting prognosis in patients with COVID-19 in the emergency department: a retrospective study
This article has 7 authors:Reviewed by ScreenIT
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Antibody response patterns in COVID‐19 patients with different levels of disease severity in Japan
This article has 15 authors:Reviewed by ScreenIT
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Prevalence of SARS-CoV-2 Contamination on Food Plant Surfaces as Determined by Environmental Monitoring
This article has 28 authors:Reviewed by ScreenIT
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Hospital Mortality and Resource Implications of Hospitalisation with COVID-19 in London, UK: A Prospective Cohort Study
This article has 16 authors:Reviewed by ScreenIT
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Molecular Mechanism of Parosmia
This article has 3 authors:Reviewed by ScreenIT