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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Clinical Characteristics in Children and Adolescents with SARS-CoV-2 Infection: Experience in a highly complex Public Hospital in the city of São Paulo
This article has 6 authors:Reviewed by ScreenIT
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Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada
This article has 4 authors:Reviewed by ScreenIT
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A Statistical and Dynamical Model for Forecasting COVID-19 Deaths based on a Hybrid Asymmetric Gaussian and SEIR Construct
This article has 1 author:Reviewed by ScreenIT
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Different Innate and Adaptive Immune Responses to SARS-CoV-2 Infection of Asymptomatic, Mild, and Severe Cases
This article has 30 authors:Reviewed by ScreenIT
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A Macroeconomic SIR Model for COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Retrospective methodology to estimate daily infections from deaths (REMEDID) in COVID-19: the Spain case study
This article has 6 authors:Reviewed by ScreenIT
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Proteomic blood profiling in mild, severe and critical COVID-19 patients
This article has 8 authors:Reviewed by ScreenIT
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Humidity and Deposition Solution Play a Critical Role in Virus Inactivation by Heat Treatment of N95 Respirators
This article has 13 authors:Reviewed by ScreenIT
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Mental health and health behaviours before and during the initial phase of the COVID-19 lockdown: longitudinal analyses of the UK Household Longitudinal Study
This article has 11 authors:Reviewed by ScreenIT
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The original and modified Caprini score equally predicts venous thromboembolism in COVID-19 patients
This article has 6 authors:Reviewed by ScreenIT