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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Diphtheria And Tetanus Vaccination History Is Associated With Lower Odds of COVID-19 Hospitalization
This article has 8 authors:Reviewed by ScreenIT
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Plasma zinc status and hyperinflammatory syndrome in hospitalized COVID-19 patients: An observational study
This article has 9 authors:Reviewed by ScreenIT
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REGEN-COV Antibody Combination in Outpatients With COVID-19 – Phase 1/2 Results
This article has 37 authors:Reviewed by ScreenIT
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Micro RNA-based regulation of genomics and transcriptomics of inflammatory cytokines in COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Data mining methodology for response to hypertension symptomology—application to COVID-19-related pharmacovigilance
This article has 7 authors:Reviewed by ScreenIT
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The Impact of Face-Masks on Total Mortality Heterogenous Effects by Gender and Age
This article has 3 authors:Reviewed by ScreenIT
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UK B.1.1.7 variant exhibits increased respiratory replication and shedding in nonhuman primates
This article has 13 authors:Reviewed by ScreenIT
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Increased risk of death in COVID-19 hospital admissions during the second wave as compared to the first epidemic wave: a prospective, single-centre cohort study in London, UK
This article has 5 authors:Reviewed by ScreenIT
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Evaluation and clinical implications of the time to a positive results of antigen testing for SARS-CoV-2
This article has 11 authors:Reviewed by ScreenIT
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COVID 19, Consumption and Inequality: A Systematic Analysis of Rural Population of India
This article has 3 authors:Reviewed by ScreenIT