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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Time series cross-correlation between home range and number of infected people during the COVID-19 pandemic in a suburban city
This article has 2 authors:Reviewed by ScreenIT
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Effectiveness of convalescent plasma therapy in COVID-19 patients with haematological malignancies
This article has 4 authors:Reviewed by ScreenIT
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Impact of SARS-CoV-2 vaccination on systemic immune responses in people living with HIV
This article has 19 authors:Reviewed by ScreenIT
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Psychosocial Predictors of COVID-19 Vaccine Uptake among Pregnant Women: A Cross-Sectional Study in Greece
This article has 7 authors:Reviewed by ScreenIT
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Mortality in Switzerland in 2021
This article has 2 authors:Reviewed by ScreenIT
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Characteristics and outcomes of COVID-19 patients during B.1.1.529 (Omicron) dominance compared to B.1.617.2 (Delta) in 89 German hospitals
This article has 11 authors:Reviewed by ScreenIT
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Limited cross-variant immune response from SARS-CoV-2 Omicron BA.2 in naïve but not previously infected outpatients
This article has 5 authors:Reviewed by ScreenIT
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THEMIS: A Framework for Cost-Benefit Analysis of COVID-19 Non-Pharmaceutical Interventions
This article has 3 authors:Reviewed by ScreenIT
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Therapeutic efficacy of four antiviral drugs in treatment of COVID-19: A protocol for systematic review and Network Meta-analysis
This article has 4 authors:Reviewed by ScreenIT
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Both COVID-19 infection and vaccination induce high-affinity cross-clade responses to SARS-CoV-2 variants
This article has 18 authors:Reviewed by ScreenIT