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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Manuscript title: Loss in the expansion of SARS-CoV-2 specific immunity is a key risk factor in fatal patients with COVID-19
This article has 8 authors:Reviewed by ScreenIT
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Impact of temperature on Covid 19 in India
This article has 1 author:Reviewed by ScreenIT
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Clinical characteristics of COVID-19 patients in Latvia under low incidence in Spring 2020
This article has 10 authors:Reviewed by ScreenIT
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COVID-19 Associated Stroke—A Single Centre Experience
This article has 26 authors:Reviewed by ScreenIT
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Burden and characteristics of COVID-19 in the United States during 2020
This article has 5 authors:Reviewed by ScreenIT
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Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays
This article has 22 authors:Reviewed by ScreenIT
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What Factors Influence Symptom Reporting and Access to Healthcare During an Emerging Infectious Disease Outbreak? A Rapid Review of the Evidence
This article has 3 authors:Reviewed by ScreenIT
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Sex Disparities and Neutralizing-Antibody Durability to SARS-CoV-2 Infection in Convalescent Individuals
This article has 24 authors:Reviewed by ScreenIT
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Face masks to prevent transmission of COVID-19: A systematic review and meta-analysis
This article has 8 authors:Reviewed by ScreenIT
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ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores
This article has 118 authors:Reviewed by ScreenIT