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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Modeling suggests that multiple immunizations or infections will reveal the benefits of updating SARS-CoV-2 vaccines
This article has 6 authors:Reviewed by ScreenIT
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Patient satisfaction with telemedicine in the Philippines during the COVID-19 pandemic
This article has 7 authors:Reviewed by ScreenIT
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Comparative Safety of the BNT162b2 Messenger RNA COVID-19 Vaccine vs Other Approved Vaccines in Children Younger Than 5 Years
This article has 13 authors:Reviewed by ScreenIT
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SARS-CoV-2 diagnostic testing rates determine the sensitivity of genomic surveillance programs
This article has 12 authors:Reviewed by ScreenIT
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Omicron and vaccines: An analysis on the decline in COVID-19 mortality
This article has 3 authors:Reviewed by ScreenIT
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Social divisions and risk perception drive divergent epidemics and large later waves
This article has 3 authors:Reviewed by ScreenIT
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Subtle cognitive impairments in memory, attention, and executive functioning in patients with post-COVID syndrome and their relationships with clinical variables and subjective complaints
This article has 10 authors:Reviewed by ScreenIT
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The SARS-CoV-2 spike protein binds and modulates estrogen receptors
This article has 31 authors:Reviewed by ScreenIT
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COVID-19 Symptoms and Duration of Rapid Antigen Test Positivity at a Community Testing and Surveillance Site During Pre-Delta, Delta, and Omicron BA.1 Periods
This article has 20 authors:Reviewed by ScreenIT
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Comparative effectiveness of sotrovimab and molnupiravir for prevention of severe COVID-19 outcomes in non-hospitalised patients: an observational cohort study using the OpenSAFELY platform
This article has 33 authors:Reviewed by ScreenIT