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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Prevalence Of COVID-19 In Rural Versus Urban Areas in a Low-Income Country: Findings from a State-Wide Study in Karnataka, India
This article has 4 authors:Reviewed by ScreenIT
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Virus Induced Lymphocytes (VIL) as a novel viral antigen-specific T cell therapy for COVID-19 and potential future pandemics
This article has 8 authors:Reviewed by ScreenIT
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Post-Discharge Health Status and Symptoms in Patients with Severe COVID-19
This article has 20 authors:Reviewed by ScreenIT
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Angiopoietin-2 Inhibition of Thrombomodulin-Mediated Anticoagulation—A Novel Mechanism That May Contribute to Hypercoagulation in Critically Ill COVID-19 Patients
This article has 10 authors:Reviewed by ScreenIT
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Clinical and Economic Effects of Widespread Rapid Testing to Decrease SARS-CoV-2 Transmission
This article has 3 authors:Reviewed by ScreenIT
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Nowcasting and forecasting provincial-level SARS-CoV-2 case positivity using google search data in South Africa
This article has 5 authors:Reviewed by ScreenIT
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Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
This article has 2 authors:Reviewed by ScreenIT
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Neurologic manifestations associated with COVID-19: a multicentre registry
This article has 96 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Epidemic Situation and Forecasting if COVID-19 in Saudi Arabia using SIR model
This article has 2 authors:Reviewed by ScreenIT
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Synthetic Data Generation for Improved covid-19 Epidemic Forecasting
This article has 4 authors:Reviewed by ScreenIT