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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Analyzing Covid-19 Data using SIRD Models
This article has 13 authors:Reviewed by ScreenIT
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Pushing beyond specifications: Evaluation of linearity and clinical performance of the cobas 6800/8800 SARS-CoV-2 RT-PCR assay for reliable quantification in blood and other materials outside recommendations
This article has 10 authors:Reviewed by ScreenIT
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An inflammatory cytokine signature predicts COVID-19 severity and survival
This article has 32 authors:Reviewed by ScreenIT
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Intensive COVID-19 testing associated with reduced mortality - an ecological analysis of 108 countries
This article has 1 author:Reviewed by ScreenIT
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Medical students perceptions and motivations in time of COVID-19 pandemic
This article has 10 authors:Reviewed by ScreenIT
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Assessing the quality, readability and reliability of online information on COVID-19
This article has 10 authors:Reviewed by ScreenIT
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Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil
This article has 7 authors:Reviewed by ScreenIT
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Mortality and use of angiotensin-converting enzyme inhibitors in COVID 19 disease: a systematic review
This article has 1 author:Reviewed by ScreenIT
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A serological assay to detect SARS-CoV-2 antibodies in at-home collected finger-prick dried blood spots
This article has 6 authors:Reviewed by ScreenIT
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Distinct inflammatory profiles distinguish COVID-19 from influenza with limited contributions from cytokine storm
This article has 19 authors:Reviewed by ScreenIT