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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Long-term manifestations and modifiers of prevalence estimates of the post-COVID-19 syndrome: A systematic review and meta-analysis
This article has 12 authors:Reviewed by ScreenIT
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Prediction of severe COVID-19 infection at the time of testing: A machine learning approach
This article has 5 authors:Reviewed by ScreenIT
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Psychiatric manifestations and associated risk factors among hospitalised patients with COVID-19 in Edo State, Nigeria: a cross-sectional study
This article has 16 authors:Reviewed by ScreenIT
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Neutralizing Antibodies and Cytokines in Breast Milk After Coronavirus Disease 2019 (COVID-19) mRNA Vaccination
This article has 7 authors:Reviewed by ScreenIT
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Increased mTOR signaling, impaired autophagic flux and cell-to-cell viral transmission are hallmarks of SARS-CoV-2 infection
This article has 31 authors:Reviewed by ScreenIT
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SARS-CoV-2 Pandemic Preventive Methods Efficacy - A Simulation Case Study
This article has 2 authors:Reviewed by ScreenIT
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Impact of SARS‐CoV‐2 infection on vaccine‐induced immune responses over time
This article has 14 authors:Reviewed by ScreenIT
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Updated Model for the USA Summer 2021 CoVID-19 Resurgence
This article has 1 author:Reviewed by ScreenIT
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Adapting Elements of Cleft Care Protocols in Low- and Middle-income Countries During and After COVID-19: A Process-driven Review With Recommendations
This article has 19 authors:Reviewed by ScreenIT
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Changes in paediatric respiratory infections at a UK teaching hospital 2016–2021; impact of the SARS-CoV-2 pandemic
This article has 16 authors:Reviewed by ScreenIT