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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Incidence and outcomes of healthcare-associated COVID-19 infections: significance of delayed diagnosis and correlation with staff absence
This article has 8 authors:Reviewed by ScreenIT
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SARS-CoV-2 Antibody Responses Are Correlated to Disease Severity in COVID-19 Convalescent Individuals
This article has 15 authors:Reviewed by ScreenIT
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Modeling the COVID-19 pandemic - parameter identification and reliability of predictions
This article has 1 author:Reviewed by ScreenIT
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Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study
This article has 8 authors:Reviewed by ScreenIT
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An improved methodology for estimating the prevalence of SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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Rhythmic components of COVID-19 daily cases in various countries
This article has 2 authors:Reviewed by ScreenIT
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The Impact of the COVID-19 Pandemic on the Uptake of Influenza Vaccine: UK-Wide Observational Study
This article has 6 authors:Reviewed by ScreenIT
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Evaluation of rapid antibody test and chest computed tomography results of COVID‐19 patients: A retrospective study
This article has 3 authors:Reviewed by ScreenIT
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CD177, a specific marker of neutrophil activation, is associated with coronavirus disease 2019 severity and death
This article has 23 authors:Reviewed by ScreenIT
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An Augmented SEIR Model with Protective and Hospital Quarantine Dynamics for the Control of COVID-19 Spread
This article has 1 author:Reviewed by ScreenIT