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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DPP-4 Inhibitors and Respiratory Infection: A Systematic Review and Meta-analysis of the Cardiovascular Outcomes Trials
This article has 9 authors:Reviewed by ScreenIT
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Molecular Dynamics Reveals the Effects of Temperature on Critical SARS-CoV-2 Proteins
This article has 2 authors:Reviewed by ScreenIT
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What does simple power law kinetics tell about our response to coronavirus pandemic?
This article has 1 author:Reviewed by ScreenIT
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A case report of COVID-19 monitoring in the Austrian professional football league
This article has 13 authors:Reviewed by ScreenIT
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Persistent fatigue following SARS-CoV-2 infection is common and independent of severity of initial infection
This article has 21 authors:Reviewed by ScreenIT
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Antibody status and cumulative incidence of SARS-CoV-2 infection among adults in three regions of France following the first lockdown and associated risk factors: a multicohort study
This article has 31 authors:Reviewed by ScreenIT
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Estimating the Case Fatality Risk of COVID-19 using Cases from Outside China
This article has 4 authors:Reviewed by ScreenIT
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Mandatory Public Health Measures for Coronavirus-19 Are Associated With Improved Mortality, Equity and Economic Outcomes
This article has 2 authors:Reviewed by ScreenIT
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Decontamination of N95 respirators against SARS-CoV-2: A scoping review
This article has 6 authors:Reviewed by ScreenIT
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Azithromycin in Hospitalised Patients with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial
This article has 35 authors:Reviewed by ScreenIT