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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Disentangling the association of hydroxychloroquine treatment with mortality in Covid-19 hospitalized patients through Hierarchical Clustering
This article has 2 authors:Reviewed by ScreenIT
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Early multidrug treatment of SARS-CoV-2 infection (COVID-19) and reduced mortality among nursing home (or outpatient/ambulatory) residents
This article has 12 authors:Reviewed by ScreenIT
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COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis
This article has 10 authors:Reviewed by ScreenIT
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The CCR5-delta32 variant might explain part of the association between COVID-19 and the chemokine-receptor gene cluster
This article has 15 authors:Reviewed by ScreenIT
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Additional analyses exploring the hypothesized transdifferentiation of plasmablasts to developing neutrophils in severe COVID-19
This article has 6 authors:Reviewed by ScreenIT
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Acceptability and feasibility of strategies to shield the vulnerable during the COVID-19 outbreak: a qualitative study in six Sudanese communities
This article has 7 authors:Reviewed by ScreenIT
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Restriction of SARS-CoV-2 replication by targeting programmed −1 ribosomal frameshifting
This article has 12 authors:Reviewed by ScreenIT
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Randomized Re-Opening of Training Facilities during the COVID-19 pandemic
This article has 17 authors:Reviewed by ScreenIT
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Early impacts of the COVID-19 pandemic on mental health care and on people with mental health conditions: framework synthesis of international experiences and responses
This article has 68 authors:Reviewed by ScreenIT
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Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images
This article has 3 authors:Reviewed by ScreenIT