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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Comparative Immunogenicity of BNT162b2 mRNA Vaccine with Natural SARS-CoV-2 Infection
This article has 26 authors:Reviewed by ScreenIT
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Mining long-COVID symptoms from Reddit: characterizing post-COVID syndrome from patient reports
This article has 2 authors:Reviewed by ScreenIT
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Anti-prothrombin autoantibodies enriched after infection with SARS-CoV-2 and influenced by strength of antibody response against SARS-CoV-2 proteins
This article has 13 authors:Reviewed by Review Commons, ScreenIT
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Prevalence evolution of SARS-CoV-2 infection in the city of São Paulo, 2020–2021
This article has 39 authors:Reviewed by ScreenIT
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Modeling recapitulates the heterogeneous outcomes of SARS-CoV-2 infection and quantifies the differences in the innate immune and CD8 T-cell responses between patients experiencing mild and severe symptoms
This article has 3 authors:Reviewed by ScreenIT
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BNT162b2 COVID-19 VACCINATION AND ITS EFFECT ON BLOOD PRESSURE
This article has 6 authors:Reviewed by ScreenIT
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Massive social protests amid the pandemic in selected Colombian cities: Did they increase COVID-19 cases?
This article has 3 authors:Reviewed by ScreenIT
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“It’s hard to keep a distance when you’re with someone you really care about”—A qualitative study of adolescents’ pandemic-related health literacy and how Covid-19 affects their lives
This article has 5 authors:Reviewed by ScreenIT
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The impact of temperature on the transmissibility and virulence of COVID-19 in Tokyo, Japan
This article has 3 authors:Reviewed by ScreenIT
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A novel hyperinflammation clinical risk tool, HI5-NEWS2, predicts mortality following early dexamethasone use in an observational cohort of hospitalised COVID-19 patients
This article has 9 authors:Reviewed by ScreenIT