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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Serological detection of 2019-nCoV respond to the epidemic: A useful complement to nucleic acid testing
This article has 13 authors:Reviewed by ScreenIT
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Caution: The clinical characteristics of COVID-19 patients at admission are changing
This article has 7 authors:Reviewed by ScreenIT
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Similarities and Differences of CT Features between COVID-19 Pneumonia and Heart Failure
This article has 12 authors:Reviewed by ScreenIT
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Human kidney is a target for novel severe acute respiratory syndrome coronavirus 2 infection
This article has 18 authors:Reviewed by ScreenIT
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Clinical characteristics of 101 COVID-19 nonsurvivors in Wuhan, China: a retrospective study
This article has 14 authors:Reviewed by ScreenIT
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Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period
This article has 5 authors:Reviewed by ScreenIT
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Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe
This article has 7 authors:Reviewed by ScreenIT
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Prediction of New Coronavirus Infection Based on a Modified SEIR Model
This article has 3 authors:Reviewed by ScreenIT
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Key Points of Clinical and CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) Imported Pneumonia Based On 21 Cases Analysis
This article has 4 authors:Reviewed by ScreenIT
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Study of the mental health status of medical personnel dealing with new coronavirus pneumonia
This article has 5 authors:Reviewed by ScreenIT