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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What explains the socioeconomic status-health gradient? Evidence from workplace COVID-19 infections
This article has 2 authors:Reviewed by ScreenIT
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COVID-19 infection risk amongst 14,104 vaccinated care home residents: a national observational longitudinal cohort study in Wales, UK, December 2020–March 2021
This article has 7 authors:Reviewed by ScreenIT
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Cross-reactivity of SARS-CoV structural protein antibodies against SARS-CoV-2
This article has 6 authors:Reviewed by ScreenIT
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The effect of COVID-19 isolation measures on the cognition and mental health of people living with dementia: A rapid systematic review of one year of quantitative evidence
This article has 4 authors:Reviewed by ScreenIT
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The impact of the COVID-19 pandemic on hospital utilisation in Sierra Leone
This article has 15 authors:Reviewed by ScreenIT
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Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study
This article has 1 author:Reviewed by ScreenIT
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Cell-Type-Specific Expression of Renin-Angiotensin-System Components in the Human Body and Its Relevance to SARS-CoV-2 Infection
This article has 7 authors:Reviewed by ScreenIT
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Novel nanostructure-coupled biosensor platform for one-step high-throughput quantification of serum neutralizing antibody after COVID-19 vaccination
This article has 15 authors:Reviewed by ScreenIT
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COVID-19 vaccine hesitancy among reproductive-aged female tier 1A healthcare workers in a United States Medical Center
This article has 7 authors:Reviewed by ScreenIT
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Factors Associated with 28-day Critical Illness Development During the First Wave of COVID-19
This article has 21 authors:Reviewed by ScreenIT