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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Impact of COVID-19 on healthcare access for Australian adolescents and young adults
This article has 4 authors:Reviewed by ScreenIT
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Comparison of different sequencing techniques for identification of SARS-CoV-2 variants of concern with multiplex real-time PCR
This article has 12 authors:Reviewed by ScreenIT
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Cellular and humoral responses to SARS-CoV-2 vaccination in immunosuppressed patients
This article has 5 authors:Reviewed by ScreenIT
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A randomized clinical trial to stimulate the cholinergic anti-inflammatory pathway in patients with moderate COVID-19-pneumonia using a slow-paced breathing technique
This article has 7 authors:Reviewed by ScreenIT
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Secreted ORF8 induces monocytic pro-inflammatory cytokines through NLRP3 pathways in patients with severe COVID-19
This article has 19 authors:Reviewed by ScreenIT
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Incidence of Guillain-Barré Syndrome After COVID-19 Vaccination in the Vaccine Safety Datalink
This article has 14 authors:Reviewed by ScreenIT
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Mental sequelae of the COVID-19 pandemic in children with and without complex medical histories and their parents: well-being prior to the outbreak and at four time-points throughout 2020 and 2021
This article has 7 authors:Reviewed by ScreenIT
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Comparing human and model-based forecasts of COVID-19 in Germany and Poland
This article has 10 authors:Reviewed by ScreenIT
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Time trends in social contacts of individuals according to comorbidity and vaccination status, before and during the COVID-19 pandemic
This article has 7 authors:Reviewed by ScreenIT
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Homologous and Heterologous Vaccine Boost Strategies for Humoral and Cellular Immunologic Coverage of the SARS-CoV-2 Omicron Variant
This article has 41 authors:Reviewed by ScreenIT