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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Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
This article has 7 authors:Reviewed by ScreenIT
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Association between Cardiovascular Burden and Requirement of Intensive Care among Patients with Mild COVID-19
This article has 28 authors:Reviewed by ScreenIT
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Impaired T cell functions along with elevated activated Tregs at the early stage of asymptomatic SARS-CoV-2 infection
This article has 14 authors:Reviewed by ScreenIT
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Web and phone-based COVID-19 syndromic surveillance in Canada: A cross-sectional study
This article has 15 authors:Reviewed by ScreenIT
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Serum SARS-COV-2 Nucleocapsid Protein: A Sensitivity and Specificity Early Diagnostic Marker for SARS-COV-2 Infection
This article has 7 authors:Reviewed by ScreenIT
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Knowledge, Attitude, and Practices (KAPs) of Community Pharmacists Regarding COVID-19: A Cross-Sectional Survey in 2 Provinces of Pakistan
This article has 10 authors:Reviewed by ScreenIT
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Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence
This article has 3 authors:Reviewed by ScreenIT
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Enveloped Virus Inactivation on Personal Protective Equipment by Exposure to Ozone
This article has 9 authors:Reviewed by ScreenIT
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The impact of thermal pasteurization on viral load and detectable live viruses in human milk and other matrices: a rapid review
This article has 4 authors:Reviewed by ScreenIT
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COVIDTrach; the outcomes of mechanically ventilated COVID-19 patients undergoing tracheostomy in the UK: Interim Report
This article has 176 authors:Reviewed by ScreenIT