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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Estimation of the Potential Impact of COVID-19 Responses on the HIV Epidemic: Analysis using the Goals Model
This article has 6 authors:Reviewed by ScreenIT
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Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
This article has 7 authors:Reviewed by ScreenIT
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Laboratory Findings Associated With Severe Illness and Mortality Among Hospitalized Individuals With Coronavirus Disease 2019 in Eastern Massachusetts
This article has 3 authors:Reviewed by ScreenIT
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Validity of a Serological Diagnostic Kit for SARS-CoV-2 Available in Iran
This article has 9 authors:Reviewed by ScreenIT
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Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: Hospitalizations, mortality, and the need for an ICU or ventilator
This article has 3 authors:Reviewed by ScreenIT
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MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Etiologic Subtypes of Ischemic Stroke in SARS-CoV-2 Patients in a Cohort of New York City Hospitals
This article has 19 authors:Reviewed by ScreenIT
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Effects of COVID-19 Home Confinement on Eating Behaviour and Physical Activity: Results of the ECLB-COVID19 International Online Survey
This article has 57 authors:Reviewed by ScreenIT
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Vitamin C for the treatment of COVID-19: A living systematic review
This article has 3 authors:Reviewed by ScreenIT
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Source and symptoms of COVID-19 among hospital workers in Milan
This article has 16 authors:Reviewed by ScreenIT