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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A chest radiography-based artificial intelligence deep-learning model to predict severe Covid-19 patient outcomes: the CAPE (Covid-19 AI Predictive Engine) Model
This article has 4 authors:Reviewed by ScreenIT
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Riesgo de COVID-19 en españoles y migrantes de distintas zonas del mundo residentes en España en la primera oleada de la enfermedad
This article has 27 authors:Reviewed by ScreenIT
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COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States
This article has 7 authors:Reviewed by ScreenIT
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Effect of moist heat reprocessing of N95 respirators on SARS-CoV-2 inactivation and respirator function
This article has 17 authors:Reviewed by ScreenIT
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The association of UV with rates of COVID-19 transmission and deaths in Mexico: the possible mediating role of vitamin D
This article has 7 authors:Reviewed by ScreenIT
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Saliva as a Candidate for COVID-19 Diagnostic Testing: A Meta-Analysis
This article has 11 authors:Reviewed by ScreenIT
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Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations
This article has 7 authors:Reviewed by ScreenIT
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Phenol-chloroform-based RNA purification for detection of SARS-CoV-2 by RT-qPCR: Comparison with automated systems
This article has 6 authors:Reviewed by ScreenIT
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Efficacy and harms of remdesivir for the treatment of COVID-19: A systematic review and meta-analysis
This article has 9 authors:Reviewed by ScreenIT