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 clade of SARS-CoV-2 viruses associated with lower viral loads in patient upper airways
This article has 11 authors:Reviewed by ScreenIT
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Strict Physical Distancing May Be More Efficient: A Mathematical Argument for Making Lockdowns Count
This article has 6 authors:Reviewed by ScreenIT
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Use of inhibitors of the renin angiotensin system and COVID-19 prognosis: a systematic review and meta-analysis
This article has 2 authors:Reviewed by ScreenIT
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Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis
This article has 1 author:Reviewed by ScreenIT
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Study Data Element Mapping: Feasibility of Defining Common Data Elements Across COVID-19 Studies
This article has 6 authors:Reviewed by ScreenIT
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Mitigation Policies and Emergency Care Management in Europe's Ground Zero for COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Paradigms about the COVID-19 pandemic: knowledge, attitudes and practices from medical students
This article has 14 authors:Reviewed by ScreenIT
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Clinical and Radiological Evaluations of COVID ‐19 Patients With Anosmia: Preliminary Report
This article has 9 authors:Reviewed by ScreenIT
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SARS-CoV-2 RNA detected in blood products from patients with COVID-19 is not associated with infectious virus
This article has 49 authors:Reviewed by ScreenIT
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Emergence of Low-density Inflammatory Neutrophils Correlates with Hypercoagulable State and Disease Severity in COVID-19 Patients
This article has 15 authors:Reviewed by ScreenIT