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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Management and Outcomes of Critically-Ill Patients with COVID-19 Pneumonia at a Safety-net Hospital in San Francisco, a Region with Early Public Health Interventions: A Case Series
This article has 22 authors:Reviewed by ScreenIT
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Clinical population genetic analysis of variants in the SARS-CoV-2 receptor ACE2
This article has 9 authors:Reviewed by ScreenIT
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Factors affecting healthcare workers’ compliance with social and behavioural infection control measures during emerging infectious disease outbreaks: rapid evidence review
This article has 4 authors:Reviewed by ScreenIT
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Modelling Capacity on London Underground Carriages in Light of UK Government Mandated Social Distancing Rules in Response to the Covid-19 Pandemic
This article has 1 author:Reviewed by ScreenIT
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Loneliness during a strict lockdown: Trajectories and predictors during the COVID-19 pandemic in 38,217 United Kingdom adults
This article has 3 authors:Reviewed by ScreenIT
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Safety-Stock: Predicting the demand for supplies in Brazilian hospitals during the COVID-19 pandemic
This article has 12 authors:Reviewed by ScreenIT
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A vulnerability index for COVID-19: spatial analysis at the subnational level in Kenya
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 (SARS-CoV-2) Ventilator Resource Management Using a Network Optimization Model and Predictive System Demand
This article has 4 authors:Reviewed by ScreenIT
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Effects of Social Distancing Measures during the First Epidemic Wave of Severe Acute Respiratory Syndrome Infection, Greece
This article has 6 authors:Reviewed by ScreenIT
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Diabetes is associated with increased risk for in-hospital mortality in patients with COVID-19: a systematic review and meta-analysis comprising 18,506 patients
This article has 8 authors:Reviewed by ScreenIT