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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The prevalence of antibodies to SARS-CoV-2 in asymptomatic healthcare workers with intensive exposure to COVID-19
This article has 5 authors:Reviewed by ScreenIT
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Hand Washing Compliance and COVID-19: A Non-Participatory Observational Study among Hospital Visitors
This article has 12 authors:Reviewed by ScreenIT
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Monocytopenia, monocyte morphological anomalies and hyperinflammation characterise severe COVID ‐19 in type 2 diabetes
This article has 15 authors:Reviewed by ScreenIT
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Individuals with Down syndrome hospitalized with COVID-19 have more severe disease
This article has 8 authors:Reviewed by ScreenIT
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Correlation of the global spread of coronavirus disease-19 with atmospheric air temperature
This article has 7 authors:Reviewed by ScreenIT
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Diagnostic accuracy of the FebriDx host response point-of-care test in patients hospitalised with suspected COVID-19
This article has 9 authors:Reviewed by ScreenIT
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Risk factors for mortality in pregnant women with SARS-CoV-2 infection
This article has 8 authors:Reviewed by ScreenIT
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The effect of ultraviolet C radiation against different N95 respirators inoculated with SARS-CoV-2
This article has 9 authors:Reviewed by ScreenIT
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Extent of pulmonary thromboembolic disease in patients with COVID-19 on CT: relationship with pulmonary parenchymal disease
This article has 7 authors:Reviewed by ScreenIT
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Compliance and containment in social distancing: mathematical modeling of COVID-19 across townships
This article has 5 authors:Reviewed by ScreenIT