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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Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia
This article has 20 authors:Reviewed by ScreenIT
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DrugWAS: Drug‐wide Association Studies for COVID‐19 Drug Repurposing
This article has 6 authors:Reviewed by ScreenIT
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Association of national Bacille Calmette-Guérin vaccination policy with COVID-19 epidemiology: an ecological study in 78 countries
This article has 1 author:Reviewed by ScreenIT
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A comparison of performance metrics for cloth masks as source control devices for simulated cough and exhalation aerosols
This article has 15 authors:Reviewed by ScreenIT
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Sex Differences in the Evolution of Neutralizing Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2
This article has 20 authors:Reviewed by ScreenIT
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Infection Control Measures and Prevalence of SARS-CoV-2 IgG among 4,554 University Hospital Employees, Munich, Germany
This article has 21 authors:Reviewed by ScreenIT
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Estimating healthcare resource needs for COVID-19 patients in Nigeria
This article has 9 authors:Reviewed by ScreenIT
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Analysis of COVID‐19 and comorbidity co‐infection model with optimal control
This article has 7 authors:Reviewed by ScreenIT
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Mathematical analysis of Córdoba calcifediol trial suggests strong role for Vitamin D in reducing ICU admissions of hospitalized COVID-19 patients
This article has 2 authors:Reviewed by ScreenIT
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U-shaped-aggressiveness of SARS-CoV-2: Period between initial symptoms and clinical progression to COVID-19 suspicion. A population-based cohort study
This article has 5 authors:Reviewed by ScreenIT