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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COVIDMED – An early pandemic randomized clinical trial of losartan treatment for hospitalized COVID-19 patients
This article has 4 authors:Reviewed by ScreenIT
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Evaluation and Modelling of the Performance of an Automated SARS-CoV-2 Antigen Assay According to Sample Type, Target Population and Epidemic Trends
This article has 10 authors:Reviewed by ScreenIT
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High-cited favorable studies for COVID-19 treatments ineffective in large trials
This article has 1 author:Reviewed by ScreenIT
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SARS-CoV-2 Omicron Neutralization After Heterologous Vaccine Boosting
This article has 37 authors:Reviewed by ScreenIT
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Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: Insights from Rapid COVID-19 Diagnosis by Adversarial Learning
This article has 4 authors:Reviewed by ScreenIT
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Prognostic value of serum/plasma neurofilament light chain for COVID ‐19‐associated mortality
This article has 11 authors:Reviewed by ScreenIT
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Mental health and sleep habits during preclinical years of medical school
This article has 6 authors:Reviewed by ScreenIT
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Genomic Biomarker Heterogeneities between SARS-CoV-2 and COVID-19
This article has 1 author:Reviewed by ScreenIT
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Covariance predicts conserved protein residue interactions important for the emergence and continued evolution of SARS-CoV-2 as a human pathogen
This article has 2 authors:Reviewed by ScreenIT
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Hydroxychloroquine/Chloroquine in COVID-19 With Focus on Hospitalized Patients – A Systematic Review
This article has 3 authors:Reviewed by ScreenIT