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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Kidney allograft recipients, immunosuppression, and coronavirus disease-2019: a report of consecutive cases from a New York City transplant center
This article has 16 authors:Reviewed by ScreenIT
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Group testing performance evaluation for SARS-CoV-2 massive scale screening and testing
This article has 1 author:Reviewed by ScreenIT
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Does weather affect the growth rate of COVID-19, a study to comprehend transmission dynamics on human health
This article has 2 authors:Reviewed by ScreenIT
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The impact of believing you have had COVID-19 on self-reported behaviour: Cross-sectional survey
This article has 6 authors:Reviewed by ScreenIT
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Sensitivity evaluation of 2019 novel coronavirus (SARS-CoV-2) RT-PCR detection kits and strategy to reduce false negative
This article has 10 authors:Reviewed by ScreenIT
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Clinical characteristics of 106 patients with neurological diseases and co-morbid coronavirus disease 2019: a retrospective study
This article has 13 authors:Reviewed by ScreenIT
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ai-corona: Radiologist-assistant deep learning framework for COVID-19 diagnosis in chest CT scans
This article has 13 authors:Reviewed by ScreenIT
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This article has 7 authors:
Reviewed by ScreenIT
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Risk factors for a positive SARS-CoV-2 PCR in patients with common cold symptoms in a primary care setting – a retrospective analysis based on a joint documentation standard
This article has 5 authors:Reviewed by ScreenIT
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Humoral response and PCR positivity in patients with COVID-19 in the New York City region, USA: an observational study
This article has 19 authors:Reviewed by ScreenIT