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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Impact of respirator versus surgical masks on SARS-CoV-2 acquisition in healthcare workers: a prospective multicentre cohort
This article has 24 authors:Reviewed by ScreenIT
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Pediatric Intensive Care Hybrid-Style Clinical Round During COVID-19 Pandemic: A Pilot Study
This article has 17 authors:Reviewed by ScreenIT
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Synergistic Block of SARS-CoV-2 Infection by Combined Drug Inhibition of the Host Entry Factors PIKfyve Kinase and TMPRSS2 Protease
This article has 7 authors:Reviewed by ScreenIT
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Patient health records and whole viral genomes from an early SARS-CoV-2 outbreak in a Quebec hospital reveal features associated with favorable outcomes
This article has 16 authors:Reviewed by ScreenIT
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Combination of a Sindbis-SARS-CoV-2 Spike Vaccine and αOX40 Antibody Elicits Protective Immunity Against SARS-CoV-2 Induced Disease and Potentiates Long-Term SARS-CoV-2-Specific Humoral and T-Cell Immunity
This article has 9 authors:Reviewed by ScreenIT
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Parental plans to vaccinate children for COVID-19 in New York city
This article has 7 authors:Reviewed by ScreenIT
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Covid-19: Comparisons by Country and Implications for Future Pandemics
This article has 11 authors:Reviewed by ScreenIT
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Self-resetting molecular probes for nucleic acids detection enabled by fuel dissipative systems
This article has 10 authors:Reviewed by ScreenIT
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COVID-19 Associated Mucormycosis: Scoping Review Protocol
This article has 6 authors:Reviewed by ScreenIT
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The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation
This article has 7 authors:Reviewed by ScreenIT