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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Forecasting COVID-19 cases using Machine Learning models
This article has 3 authors:Reviewed by ScreenIT
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Assessing the Burden of Coronavirus Disease 2019 (COVID-19) Among Healthcare Workers in Mexico City: A Data-Driven Call to Action
This article has 7 authors:Reviewed by ScreenIT
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The mental health impact of the covid-19 pandemic on healthcare workers, and interventions to help them: A rapid systematic review
This article has 9 authors:Reviewed by ScreenIT
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Profiling Cases With Nonrespiratory Symptoms and Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infections in Mexico City
This article has 6 authors:Reviewed by ScreenIT
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The effect of opening up the US on COVID-19 spread
This article has 2 authors:Reviewed by ScreenIT
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Early COVID-19 Interventions Failed to Replicate 1918 St. Louis vs. Philadelphia Outcomes in the United States
This article has 3 authors:Reviewed by ScreenIT
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Health literacy in the general population in the context of epidemic or pandemic coronavirus outbreak situations: Rapid scoping review
This article has 8 authors:Reviewed by ScreenIT
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The impact of COVID-19 on adjusted mortality risk in care homes for older adults in Wales, UK: a retrospective population-based cohort study for mortality in 2016–2020
This article has 8 authors:Reviewed by ScreenIT
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Age Matters: COVID-19 Prevalence in a Vaping Adolescent Population – An Observational Study
This article has 11 authors:Reviewed by ScreenIT
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Robust and sensitive detection of SARS-CoV-2 using PCR based methods
This article has 17 authors:Reviewed by ScreenIT