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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Risk of severe COVID-19 from the Delta and Omicron variants in relation to vaccination status, sex, age and comorbidities – surveillance results from southern Sweden, July 2021 to January 2022
This article has 7 authors:Reviewed by ScreenIT
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Fairness and efficiency considerations in COVID-19 vaccine allocation strategies: A case study comparing front-line workers and 65–74 year olds in the United States
This article has 4 authors:Reviewed by ScreenIT
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Psychological, endocrine and polygenic predictors of emotional well-being during the COVID-19 pandemic in a longitudinal birth cohort
This article has 13 authors:Reviewed by ScreenIT
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Longitudinal Analysis of Biologic Correlates of COVID-19 Resolution: Case Report
This article has 18 authors:Reviewed by ScreenIT
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Replicating RNA platform enables rapid response to the SARS-CoV-2 Omicron variant and elicits enhanced protection in naïve hamsters compared to ancestral vaccine
This article has 22 authors:Reviewed by ScreenIT
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Sentinel Cards Provide Practical SARS-CoV-2 Monitoring in School Settings
This article has 35 authors:Reviewed by ScreenIT
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COVID ‐19 Infection Enhances Susceptibility to Oxidative Stress–Induced Parkinsonism
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 Vaccination is Associated with Decreasing Cases, Hospitalizations, and Deaths Across Age Groups and Variants over 9 months in Switzerland
This article has 1 author:Reviewed by ScreenIT
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Impact of Severe Acute Respiratory Syndrome Coronavirus 2 Variants on Inpatient Clinical Outcome
This article has 17 authors:Reviewed by ScreenIT
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MACHINE LEARNING IMPACT ASSESSMENT OF CLIMATE FACTORS ON DAILY COVID-19 CASES
This article has 6 authors:Reviewed by ScreenIT