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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ACE inhibitors, AT1 receptor blockers and COVID-19: clinical epidemiology evidences for a continuation of treatments. The ACER-COVID study
This article has 14 authors:Reviewed by ScreenIT
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Estimating the impact of virus testing strategies on the COVID-19 case fatality rate using fixed-effects models
This article has 5 authors:Reviewed by ScreenIT
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Informative Ranking of Stand Out Collections of Symptoms: A New Data-Driven Approach to Identify the Strong Warning Signs of COVID 19
This article has 2 authors:Reviewed by ScreenIT
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Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates
This article has 7 authors:Reviewed by ScreenIT
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Inequalities in COVID19 mortality related to ethnicity and socioeconomic deprivation
This article has 7 authors:Reviewed by ScreenIT
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Genetic structure of SARS-CoV-2 reflects clonal superspreading and multiple independent introduction events, North-Rhine Westphalia, Germany, February and March 2020
This article has 17 authors:Reviewed by ScreenIT
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Poolkeh Finds the Optimal Pooling Strategy for a Population-wide COVID-19 Testing (Israel, UK, and US as Test Cases)
This article has 3 authors:Reviewed by ScreenIT
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Inferred duration of infectious period of SARS-CoV-2: rapid scoping review and analysis of available evidence for asymptomatic and symptomatic COVID-19 cases
This article has 14 authors:Reviewed by ScreenIT
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Evaluation of “stratify and shield” as a policy option for ending the COVID-19 lockdown in the UK
This article has 2 authors:Reviewed by ScreenIT
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Modelling of Systemic versus Pulmonary Chloroquine Exposure in Man for COVID-19 Dose Selection
This article has 9 authors:Reviewed by ScreenIT