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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Workplace contact patterns in England during the COVID-19 pandemic: Analysis of the Virus Watch prospective cohort study
This article has 15 authors:Reviewed by ScreenIT
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S Gene Target Failure (SGTF) in Commercial Multiplex RT-PCR assay as indicator to detect SARS-CoV-2 VOC B.1.1.7 lineage in Tamil Nadu, India
This article has 11 authors:Reviewed by ScreenIT
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COVID-19 vaccination and Guillain-Barré syndrome: analyses using the National Immunoglobulin Database
This article has 61 authors:Reviewed by ScreenIT
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Forecasting COVID-19 infection trends in the EU-27 countries, the UK and Switzerland due to SARS-CoV-2 Variant of Concern Omicron
This article has 4 authors:Reviewed by ScreenIT
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Dynamics of anti-Spike IgG antibody level after the second BNT162b2 COVID-19 vaccination in health care workers
This article has 3 authors:Reviewed by ScreenIT
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Final sizes and durations of new COVID-19 pandemic waves in Poland and Germany predicted by generalized SIR model
This article has 1 author:Reviewed by ScreenIT
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Efficient control of IL-6, CRP and Ferritin in Covid-19 patients with two variants of Beta-1,3-1,6 glucans in combination, within 15 days in an open-label prospective randomized clinical trial
This article has 10 authors:Reviewed by ScreenIT
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Combining predictive models with future change scenarios can produce credible forecasts of COVID-19 futures
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 amplified racial disparities in the U.S. criminal legal system
This article has 12 authors:Reviewed by ScreenIT
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Heterogenous humoral and cellular immune responses with distinct trajectories post-SARS-CoV-2 infection in a population-based cohort
This article has 15 authors:Reviewed by ScreenIT