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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Pregnancy and Breastfeeding During COVID-19 Pandemic: A Systematic Review of Published Pregnancy Cases
This article has 4 authors:Reviewed by ScreenIT
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Strong effect of socioeconomic levels on the spread and treatment of the 2019 novel coronavirus (COVID-19) in China
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 Outcomes in Saudi Arabia and the UK: A Tale of Two Kingdoms
This article has 7 authors:Reviewed by ScreenIT
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On the reliability of model-based predictions in the context of the current COVID epidemic event: impact of outbreak peak phase and data paucity
This article has 4 authors:Reviewed by ScreenIT
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Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil
This article has 42 authors:Reviewed by ScreenIT
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Impact of the COVID-19 pandemic and initial period of lockdown on the mental health and well-being of adults in the UK
This article has 2 authors:Reviewed by ScreenIT
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Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT
This article has 6 authors:Reviewed by ScreenIT
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Concentration-dependent mortality of chloroquine in overdose
This article has 7 authors: -
Estimating COVID-19 Antibody Seroprevalence in Santa Clara County, California. A re-analysis of Bendavid et al.
This article has 2 authors:Reviewed by ScreenIT
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EXPLAINABLE-BY-DESIGN APPROACH FOR COVID-19 CLASSIFICATION VIA CT-SCAN
This article has 2 authors:Reviewed by ScreenIT