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
-
Importance of untested infectious individuals for interventions to suppress COVID-19
This article has 3 authors:Reviewed by ScreenIT
-
Lymphocytopaenia is associated with severe SARS-CoV-2 disease: A Systematic Review and Meta-Analysis of Clinical Data
This article has 12 authors:Reviewed by ScreenIT
-
REGIONAL DETERMINANTS OF THE EXPANSION OF COVID-19 IN BRAZIL
This article has 3 authors:Reviewed by ScreenIT
-
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
This article has 7 authors:Reviewed by ScreenIT
-
Machine Learning Analysis of Chest CT Scan Images as a Complementary Digital Test of Coronavirus (COVID-19) Patients
This article has 4 authors:Reviewed by ScreenIT
-
Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
This article has 9 authors:Reviewed by ScreenIT
-
Partial unlock for COVID-19-like epidemics can save 1-3 million lives worldwide
This article has 3 authors:Reviewed by ScreenIT
-
The Prediction for the Outbreak of COVID-19 for 15 States in USA by Using Turning Phase Concepts as of April 10, 2020
This article has 5 authors:Reviewed by ScreenIT
-
Adaptive split ventilator system enables parallel ventilation, individual monitoring and ventilation pressures control for each lung simulators
This article has 10 authors:Reviewed by ScreenIT
-
Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19
This article has 4 authors:Reviewed by ScreenIT