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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"Sample pooling of RNA extracts to speed up SARS-CoV-2 diagnosis using CDC FDA EUA RT-qPCR kit"
This article has 5 authors:Reviewed by ScreenIT
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Deep learning model to predict the need for mechanical ventilation using chest X-ray images in hospitalised patients with COVID-19
This article has 14 authors:Reviewed by ScreenIT
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Stress-related emotional and behavioural impact following the first COVID-19 outbreak peak
This article has 12 authors:Reviewed by ScreenIT
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A multiagent coronavirus model with territorial vulnerability parameters
This article has 3 authors:Reviewed by ScreenIT
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Diagnostic accuracy of Panbio rapid antigen tests on oropharyngeal swabs for detection of SARS-CoV-2
This article has 17 authors:Reviewed by ScreenIT
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Central and peripheral nervous system complications of COVID-19: a prospective tertiary center cohort with 3-month follow-up
This article has 21 authors:Reviewed by ScreenIT
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SARS-CoV-2 viral RNA load dynamics in the nasopharynx of infected children
This article has 10 authors:Reviewed by ScreenIT
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Seroprevalence of the SARS-CoV-2 infection in health workers of the Sanitary Region VIII, at province of Buenos Aires
This article has 12 authors:Reviewed by ScreenIT
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A Contact-Explicit Covid-19 Epidemic and Response Assessment Model
This article has 3 authors:Reviewed by ScreenIT
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Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia
This article has 13 authors:Reviewed by ScreenIT