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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Environmental indicator for effective control of COVID-19 spreading
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 In Shang Hai: It is Worth Learning from the Successful Experience in Preventing and Controlling the Overseas Epidemic Situation
This article has 3 authors:Reviewed by ScreenIT
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National estimates of critical care capacity in 54 African countries
This article has 3 authors:Reviewed by ScreenIT
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COVID-19: age, Interleukin-6, C-reactive protein, and lymphocytes as key clues from a multicentre retrospective study
This article has 37 authors:Reviewed by ScreenIT
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COVID-19 healthcare demand projections: Arizona
This article has 6 authors:Reviewed by ScreenIT
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A Municipality-Based Approach Using Commuting Census Data to Characterize the Vulnerability to Influenza-Like Epidemic: The COVID-19 Application in Italy
This article has 4 authors:Reviewed by ScreenIT
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Improved survival outcome in SARs-CoV-2 (COVID-19) Acute Respiratory Distress Syndrome patients with Tocilizumab administration
This article has 13 authors:Reviewed by ScreenIT
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Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty
This article has 3 authors:Reviewed by ScreenIT
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Adjusted fatality rates of COVID19 pandemic: a comparison across countries
This article has 4 authors:Reviewed by ScreenIT
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Multilevel Integrated Model with a Novel Systems Approach (MIMANSA) for Simulating the Spread of COVID-19
This article has 4 authors:Reviewed by ScreenIT