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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Predicting severity of Covid-19 using standard laboratory parameters
This article has 3 authors:Reviewed by ScreenIT
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Municipality-Level Predictors of COVID-19 Mortality in Mexico: A Cautionary Tale
This article has 5 authors:Reviewed by ScreenIT
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VIP plasma levels associate with survival in severe COVID-19 patients, correlating with protective effects in SARS-CoV-2-infected cells
This article has 17 authors:Reviewed by ScreenIT
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SARS-CoV2 spike protein displays biologically significant similarities with paramyxovirus surface proteins; a bioinformatics study
This article has 4 authors:Reviewed by ScreenIT
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Excess Risk of COVID-19 to University Populations Resulting from In-Person Sporting Events
This article has 6 authors:Reviewed by ScreenIT
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Predicting the impact of COVID-19 interruptions on transmission of gambiense human African trypanosomiasis in two health zones of the Democratic Republic of Congo
This article has 9 authors:Reviewed by ScreenIT
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Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)
This article has 7 authors:Reviewed by ScreenIT
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State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures
This article has 5 authors:Reviewed by ScreenIT
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Deep learning for predicting COVID-19 malignant progression
This article has 15 authors:Reviewed by ScreenIT
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Epidemiological and clinical characteristics of cancer patients with COVID-19: A systematic review and meta-analysis of global data
This article has 15 authors:Reviewed by ScreenIT