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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Cardiovascular diseases burden in COVID-19: Systematic review and meta-analysis
This article has 11 authors:Reviewed by ScreenIT
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Incidence, clinical outcomes, and transmission dynamics of severe coronavirus disease 2019 in California and Washington: prospective cohort study
This article has 14 authors:Reviewed by ScreenIT
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An improved mathematical prediction of the time evolution of the Covid-19 pandemic in Italy, with a Monte Carlo simulation and error analyses
This article has 2 authors:Reviewed by ScreenIT
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Smoking Is Associated With COVID-19 Progression: A Meta-analysis
This article has 2 authors:Reviewed by ScreenIT
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Vapor H 2 O 2 sterilization as a decontamination method for the reuse of N95 respirators in the COVID-19 emergency
This article has 15 authors:Reviewed by ScreenIT
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Designing a multi-epitope peptide-based vaccine against SARS-CoV-2
This article has 4 authors:Reviewed by ScreenIT
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Delayed‐phase thrombocytopenia in patients with coronavirus disease 2019 (COVID‐19)
This article has 10 authors:Reviewed by ScreenIT
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Clinical characteristics of 34 COVID-19 patients admitted to intensive care unit in Hangzhou, China
This article has 8 authors:Reviewed by ScreenIT
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COVID-19: A model correlating BCG vaccination to protection from mortality implicates trained immunity
This article has 9 authors:Reviewed by ScreenIT
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Clinical Efficacy of Intravenous Immunoglobulin Therapy in Critical Patients with COVID-19: A Multicenter Retrospective Cohort Study
This article has 14 authors:Reviewed by ScreenIT