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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Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19
This article has 15 authors:Reviewed by ScreenIT
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Clinical laboratory parameters associated with severe or critical novel coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis
This article has 7 authors:Reviewed by ScreenIT
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Gender-Based Disparities in COVID-19 Patient Outcomes: A Propensity-matched Analysis
This article has 4 authors:Reviewed by ScreenIT
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Who is at the highest risk from COVID-19 in India? Analysis of health, healthcare access, and socioeconomic indicators at the district level
This article has 3 authors:Reviewed by ScreenIT
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Laboratory Findings of COVID-19 Infection are Conflicting in Different Age Groups and Pregnant Women: A Literature Review
This article has 7 authors:Reviewed by ScreenIT
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Intra-host site-specific polymorphisms of SARS-CoV-2 is consistent across multiple samples and methodologies
This article has 7 authors:Reviewed by ScreenIT
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Cell-based therapies for COVID-19: A living, systematic review
This article has 3 authors:Reviewed by ScreenIT
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Mathematical Analysis of a COVID-19 Epidemic Model by Using Data Driven Epidemiological Parameters of Diseases Spread in India
This article has 4 authors:Reviewed by ScreenIT
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Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment
This article has 4 authors:Reviewed by ScreenIT
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First detection of SARS-CoV-2 in untreated wastewaters in Italy
This article has 8 authors:Reviewed by ScreenIT