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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Almitrine as a non-ventilatory strategy to improve intrapulmonary shunt in COVID-19 patients
This article has 5 authors:Reviewed by ScreenIT
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A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation
This article has 12 authors:Reviewed by ScreenIT
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MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic
This article has 9 authors:Reviewed by ScreenIT
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Pathophysiology of SARS-CoV-2: the Mount Sinai COVID-19 autopsy experience
This article has 54 authors:Reviewed by ScreenIT
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Performance of six SARS-CoV-2 immunoassays in comparison with microneutralisation
This article has 10 authors:Reviewed by ScreenIT
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Dramatic reduction of psychiatric emergency consultations during lockdown linked to COVID ‐19 in Paris and suburbs
This article has 16 authors:Reviewed by ScreenIT
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Potently neutralizing and protective human antibodies against SARS-CoV-2
This article has 41 authors:Reviewed by ScreenIT
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Serologic responses to SARS-CoV-2 infection among hospital staff with mild disease in eastern France
This article has 26 authors:Reviewed by ScreenIT
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Risk stratification of hospitalized COVID-19 patients through comparative studies of laboratory results with influenza
This article has 7 authors:Reviewed by ScreenIT
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Early risk assessment for COVID-19 patients from emergency department data using machine learning
This article has 19 authors:Reviewed by ScreenIT