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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Anti-membrane Antibodies Persist at Least One Year and Discriminate Between Past Coronavirus Disease 2019 Infection and Vaccination
This article has 12 authors:Reviewed by ScreenIT
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Immunogenicity and safety of the homogenous booster shot of a recombinant fusion protein vaccine (V-01) against COVID-19 in healthy adult participants primed with a two-dose regimen
This article has 20 authors:Reviewed by ScreenIT
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Safety and immunogenicity of heterologous and homologous inactivated and adenoviral-vectored COVID-19 vaccine regimens in healthy adults: a prospective cohort study
This article has 12 authors:Reviewed by ScreenIT
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Evaluation of patients treated by telemedicine in the COVID-19 pandemic by a private clinic in São Paulo, Brazil: A non-randomized clinical trial preliminary study
This article has 13 authors:Reviewed by ScreenIT
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Development of a mathematical model to predict the health impact and duration of SARS-CoV-2 outbreaks on board cargo vessels
This article has 6 authors:Reviewed by ScreenIT
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Cellular and humoral immune response to mRNA COVID-19 vaccination in subjects with chronic lymphocytic leukemia
This article has 12 authors:Reviewed by ScreenIT
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SARS-CoV-2 Beta and Delta variants trigger Fc effector function with increased cross-reactivity
This article has 19 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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The United States COVID-19 Forecast Hub dataset
This article has 428 authors:Reviewed by ScreenIT
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Severity and inpatient mortality of COVID-19 pneumonia from Beta variant infection: a clinical cohort study in Cape Town, South Africa
This article has 19 authors:Reviewed by ScreenIT
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Machine Learning in the analysis of lethality and evolution of infection by the SARS-CoV-2 virus (COVID-19) in workers of the Mexico City Metro
This article has 4 authors:Reviewed by ScreenIT