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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Evaluation of Turkish social distancing measures on the spread of COVID-19
This article has 1 author:Reviewed by ScreenIT
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Global academic response to COVID ‐19: Cross‐sectional study
This article has 6 authors:Reviewed by ScreenIT
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Distinguishing L and H phenotypes of COVID-19 using a single x-ray image
This article has 2 authors:Reviewed by ScreenIT
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Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection
This article has 33 authors:Reviewed by ScreenIT
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Pre-test probability for SARS-Cov-2-related infection score: The PARIS score
This article has 18 authors:Reviewed by ScreenIT
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Extrafollicular B cell responses correlate with neutralizing antibodies and morbidity in COVID-19
This article has 33 authors:Reviewed by ScreenIT
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Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool
This article has 3 authors:Reviewed by ScreenIT
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Mental Health Status of the General Population during the COVID-19 Pandemic: A Cross-Sectional National Survey in Japan
This article has 4 authors:Reviewed by ScreenIT
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Simulating a Community Mental Health Service During the COVID-19 Pandemic: Effects of Clinician–Clinician Encounters, Clinician–Patient–Family Encounters, Symptom-Triggered Protective Behaviour, and Household Clustering
This article has 13 authors:Reviewed by ScreenIT
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Risk prediction for poor outcome and death in hospital in-patients with COVID-19: derivation in Wuhan, China and external validation in London, UK
This article has 15 authors:Reviewed by ScreenIT