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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Highly pathogenic coronavirus N protein aggravates inflammation by MASP-2-mediated lectin complement pathway overactivation
This article has 32 authors:Reviewed by ScreenIT
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Global, Regional and National Incidence and Case-fatality rates of Novel Coronavirus (COVID-19) across 154 countries and territories: A systematic assessment of cases reported from January to March 16, 2020
This article has 5 authors:Reviewed by ScreenIT
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SARS-CoV-2 detection using digital PCR for COVID-19 diagnosis, treatment monitoring and criteria for discharge
This article has 6 authors:Reviewed by ScreenIT
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Medical treatment of 55 patients with COVID-19 from seven cities in northeast China who fully recovered
This article has 7 authors:Reviewed by ScreenIT
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Causal empirical estimates suggest COVID-19 transmission rates are highly seasonal
This article has 2 authors:Reviewed by ScreenIT
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Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study
This article has 11 authors:Reviewed by ScreenIT
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Effectiveness of isolation policies in schools: evidence from a mathematical model of influenza and COVID-19
This article has 2 authors:Reviewed by ScreenIT
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Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19
This article has 15 authors:Reviewed by ScreenIT
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A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19
This article has 2 authors:Reviewed by ScreenIT
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“The more I fear about COVID-19, the more I wear medical masks”: A survey on risk perception and medical masks’ uses
This article has 1 author:Reviewed by ScreenIT