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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Impact of Hypertension on Progression and Prognosis in Patients with COVID-19 A Retrospective Cohort Study in 1031 Hospitalized Cases in Wuhan, China
This article has 8 authors:Reviewed by ScreenIT
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Clinical characteristics and outcomes of venous thromboembolism in patients hospitalized for COVID-19: Systematic review and meta-analysis
This article has 8 authors:Reviewed by ScreenIT
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Evaluation for SARS-CoV-2 in Breast Milk From 18 Infected Women
This article has 7 authors:Reviewed by ScreenIT
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Clinical characteristics of coronavirus disease 2019 (COVID-19) patients in Kuwait
This article has 15 authors:Reviewed by ScreenIT
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Critical complications of COVID-19: A descriptive meta-analysis study
This article has 7 authors:Reviewed by ScreenIT
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Detecting space–time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities
This article has 6 authors:Reviewed by ScreenIT
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Innate immune signaling in the olfactory epithelium reduces odorant receptor levels: modeling transient smell loss in COVID-19 patients
This article has 17 authors:Reviewed by ScreenIT
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Air Quality and COVID-19 Prevalence/Fatality
This article has 6 authors:Reviewed by ScreenIT
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Risk factors for critical-ill events of patients with COVID-19 in Wuhan, China: a retrospective cohort study
This article has 10 authors:Reviewed by ScreenIT
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Assessing the burden of COVID-19 in Canada
This article has 2 authors:Reviewed by ScreenIT