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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The efficacy and safety of monoclonal antibody treatments against COVID-19: A systematic review and meta-analysis of randomized clinical trials
This article has 6 authors:Reviewed by ScreenIT
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A systematic review and meta‐analysis of the mental health symptoms during the Covid‐19 pandemic in S outheast A sia
This article has 13 authors:Reviewed by ScreenIT
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Comparison of antigen- and RT-PCR-based testing strategies for detection of SARS-CoV-2 in two high-exposure settings
This article has 8 authors:Reviewed by ScreenIT
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Highly sensitive scent-detection of COVID-19 patients in vivo by trained dogs
This article has 13 authors:Reviewed by ScreenIT
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Transmission of SARS-CoV-2 associated with aircraft travel: a systematic review
This article has 9 authors:Reviewed by ScreenIT
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Efficacy of clarithromycin in patients with mild COVID-19 pneumonia not receiving oxygen administration: protocol for an exploratory, multicentre, open-label, randomised controlled trial (CAME COVID-19 study)
This article has 9 authors:Reviewed by ScreenIT
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Virtual care and the impact of COVID‐19 on nursing: A single centre evaluation
This article has 4 authors:Reviewed by ScreenIT
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Vaccine nationalism and the dynamics and control of SARS-CoV-2
This article has 13 authors:Reviewed by ScreenIT
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Classifying Texas counties using ARIMA Models on COVID-19 daily confirmed cases: the impact of political affiliation and face covering orders
This article has 3 authors:Reviewed by ScreenIT
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A cluster-based model of COVID-19 transmission dynamics
This article has 2 authors:Reviewed by ScreenIT