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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Effect of underlying comorbidities on the infection and severity of COVID-19 in South Korea
This article has 13 authors:Reviewed by ScreenIT
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Rapid review of available evidence on the serial interval and generation time of COVID-19
This article has 10 authors:Reviewed by ScreenIT
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Low Albumin Levels Are Associated with Poorer Outcomes in a Case Series of COVID-19 Patients in Spain: A Retrospective Cohort Study
This article has 10 authors:Reviewed by ScreenIT
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Pandemic-related health literacy: a systematic review of literature in COVID-19, SARS and MERS pandemics
This article has 5 authors:Reviewed by ScreenIT
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Characteristics of 1573 healthcare workers who underwent nasopharyngeal swab testing for SARS-CoV-2 in Milan, Lombardy, Italy
This article has 16 authors:Reviewed by ScreenIT
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Delayed healthcare seeking and prolonged illness in healthcare workers during the COVID-19 pandemic: a single-centre observational study
This article has 8 authors:Reviewed by ScreenIT
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Chaos, percolation and the coronavirus spread: a two-step model
This article has 2 authors:Reviewed by ScreenIT
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Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study
This article has 19 authors:Reviewed by ScreenIT
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Time-resolving the COVID-19 outbreak using frequency domain analysis
This article has 2 authors:Reviewed by ScreenIT
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Age-stratified discrete compartment model of the COVID-19 epidemic with application to Switzerland
This article has 2 authors:Reviewed by ScreenIT