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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Modeling COVID19 mortality in the US: Community context and mobility matter
This article has 4 authors:Reviewed by ScreenIT
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SARS-CoV-2–Specific Antibody Detection for Seroepidemiology: A Multiplex Analysis Approach Accounting for Accurate Seroprevalence
This article has 9 authors:Reviewed by ScreenIT
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IL-13 is a driver of COVID-19 severity
This article has 27 authors:Reviewed by ScreenIT
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The clinical spectrum of encephalitis in COVID-19 disease: the ENCOVID multicentre study
This article has 29 authors:Reviewed by ScreenIT
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Clinical outcomes of hospitalised patients with COVID-19 and chronic inflammatory and autoimmune rheumatic diseases: a multicentric matched cohort study
This article has 15 authors:Reviewed by ScreenIT
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Kidney function on admission predicts in-hospital mortality in COVID-19
This article has 13 authors:Reviewed by ScreenIT
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A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images
This article has 3 authors:Reviewed by ScreenIT
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The methodological quality is insufficient in clinical practice guidelines in the context of COVID-19: systematic review
This article has 23 authors:Reviewed by ScreenIT
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COVID-19 preventive behaviours among people with anxiety and depressive symptoms: findings from Japan
This article has 4 authors:Reviewed by ScreenIT
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Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom
This article has 8 authors:Reviewed by ScreenIT