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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Immune Phenotyping Based on the Neutrophil-to-Lymphocyte Ratio and IgG Level Predicts Disease Severity and Outcome for Patients With COVID-19
This article has 11 authors:Reviewed by ScreenIT
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Influence factors of death risk among COVID-19 patients in Wuhan, China: a hospital-based case-cohort study
This article has 12 authors:Reviewed by ScreenIT
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Triaging patients in the outbreak of the 2019 novel coronavirus
This article has 23 authors:Reviewed by ScreenIT
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Environmental contamination of SARS-CoV-2 in healthcare premises
This article has 15 authors:Reviewed by ScreenIT
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Immunopathological characteristics of coronavirus disease 2019 cases in Guangzhou, China
This article has 7 authors:Reviewed by ScreenIT
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Estimation of the final size of the second phase of the coronavirus COVID 19 epidemic by the logistic model
This article has 1 author:Reviewed by ScreenIT
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Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world
This article has 4 authors:Reviewed by ScreenIT
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Clinical features and outcomes of 2019 novel coronavirus–infected patients with cardiac injury
This article has 9 authors:Reviewed by ScreenIT
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Active or latent tuberculosis increases susceptibility to COVID-19 and disease severity
This article has 11 authors:Reviewed by ScreenIT
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Strongly Heterogeneous Transmission of COVID-19 in Mainland China: Local and Regional Variation
This article has 2 authors:Reviewed by ScreenIT