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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Chloroquine and hydroxychloroquine effectiveness in human subjects during coronavirus: a systematic review
This article has 8 authors:Reviewed by ScreenIT
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APOE e4 Genotype Predicts Severe COVID-19 in the UK Biobank Community Cohort
This article has 7 authors:Reviewed by ScreenIT
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Voices from the frontline: findings from a thematic analysis of a rapid online global survey of maternal and newborn health professionals facing the COVID-19 pandemic
This article has 24 authors:Reviewed by ScreenIT
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Observer agreement and clinical significance of chest CT reporting in patients suspected of COVID-19
This article has 16 authors:Reviewed by ScreenIT
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Youtube as an Information Source During the Coronavirus Disease (COVID-19) Pandemic: Evaluation of the Turkish and English Content
This article has 6 authors:Reviewed by ScreenIT
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Machine learning-based prediction of COVID-19 diagnosis based on symptoms
This article has 3 authors:Reviewed by ScreenIT
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A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images
This article has 5 authors:Reviewed by ScreenIT
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Relationship Between Odor Intensity Estimates and COVID-19 Prevalence Prediction in a Swedish Population
This article has 25 authors:Reviewed by ScreenIT
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Development and external validation of a prognostic tool for COVID-19 critical disease
This article has 16 authors:Reviewed by ScreenIT
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The contribution of asymptomatic SARS-CoV-2 infections to transmission on the Diamond Princess cruise ship
This article has 48 authors:Reviewed by ScreenIT