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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Seasonal human coronavirus antibodies are boosted upon SARS-CoV-2 infection but not associated with protection
This article has 38 authors:Reviewed by ScreenIT
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A novel predictive mathematical model for COVID-19 pandemic with quarantine, contagion dynamics, and environmentally mediated transmission
This article has 4 authors:Reviewed by ScreenIT
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Association between corticosteroids and intubation or death among patients with COVID-19 pneumonia in non-ICU settings: an observational study using of real-world data from 51 hospitals in France and Luxembourg
This article has 11 authors:Reviewed by ScreenIT
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COVID-19 behavioural insights study: Preliminary findings from Finland, April-May, 2020
This article has 4 authors:Reviewed by ScreenIT
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Estimating population immunity without serological testing
This article has 1 author:Reviewed by ScreenIT
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Detection of asymptomatic SARS-CoV-2 infections among healthcare workers: results from a large-scale screening program based on rapid serological testing
This article has 22 authors:Reviewed by ScreenIT
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Methylprednisolone in adults hospitalized with COVID-19 pneumonia
This article has 18 authors:Reviewed by ScreenIT
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Global prediction of unreported SARS-CoV2 infection from observed COVID-19 cases
This article has 4 authors:Reviewed by ScreenIT
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Shift work is associated with positive COVID-19 status in hospitalised patients
This article has 6 authors:Reviewed by ScreenIT
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Comparative analysis of antigen-specific anti-SARS-CoV-2 antibody isotypes in COVID-19 patients
This article has 13 authors:Reviewed by ScreenIT