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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Infectious viral shedding of SARS-CoV-2 Delta following vaccination: A longitudinal cohort study
This article has 38 authors:Reviewed by ScreenIT
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How should COVID-19 vaccines be distributed between the Global North and South: a discrete choice experiment in six European countries
This article has 4 authors:This article has been curated by 1 group: -
Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases
This article has 13 authors:Reviewed by ScreenIT
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Sequence Proven Reinfections with SARS-CoV-2 at a Large Academic Center
This article has 10 authors:Reviewed by ScreenIT
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Assessment of the humoral response to the homologous Gam-COVID-Vac (Sputnik V) or heterologous Sputnik V/mRNA-1273 (Moderna) vaccination against SARS-CoV-2 in dialysis patients
This article has 12 authors:Reviewed by ScreenIT
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Neutralization Escape by SARS-CoV-2 Omicron Subvariants BA.2.12.1, BA.4, and BA.5
This article has 11 authors:Reviewed by ScreenIT
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Self-medication Practices and Associated Factors among COVID-19 Recovered Patients to Prevent Future Infections: A Web-based Survey in Bangladesh
This article has 7 authors:Reviewed by ScreenIT
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Analytical performance of rapid antigen tests for the detection of SARS-CoV-2 during widespread circulation of the Omicron variant
This article has 12 authors:Reviewed by ScreenIT
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Preceding anti-spike IgG levels predicted risk and severity of COVID-19 during the Omicron-dominant wave in Santa Fe city, Argentina
This article has 25 authors:Reviewed by ScreenIT
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Real-world effectiveness of casirivimab and imdevimab among patients diagnosed with COVID-19 in the ambulatory setting: a retrospective cohort study using a large claims database
This article has 9 authors:Reviewed by ScreenIT