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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A Multivariate Spatiotemporal Model of COVID-19 Epidemic Using Ensemble of ConvLSTM Networks
This article has 3 authors:Reviewed by ScreenIT
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Analysis and Estimation of Length of In-Hospital Stay Using Demographic Data of COVID-19 Recovered Patients in Singapore
This article has 3 authors:Reviewed by ScreenIT
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Differential Ventilation Using Flow Control Valves as a Potential Bridge to Full Ventilatory Support during the COVID-19 Crisis
This article has 8 authors:Reviewed by ScreenIT
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Estimating the probability of New Zealand regions being free from COVID-19 using a stochastic SEIR model
This article has 1 author:Reviewed by ScreenIT
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COVID-19: An Update on the Epidemiological, Genomic Origin, Phylogenetic study, India centric to Worldwide current status
This article has 2 authors:Reviewed by ScreenIT
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Effects of medical resource capacities and intensities of public mitigation measures on outcomes of COVID-19 outbreaks
This article has 11 authors:Reviewed by ScreenIT
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Lies, Gosh Darn Lies, and not enough good statistics: why epidemic model parameter estimation fails
This article has 3 authors:Reviewed by ScreenIT
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COVID-19 Epidemic Dynamics and Population Projections from Early Days of Case Reporting in a 40 million population from Southern India
This article has 4 authors:Reviewed by ScreenIT
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A Randomized, Single-Blind, Group Sequential, Active-Controlled Study to Evaluate the Clinical Efficacy and Safety of α-Lipoic Acid for Critically Ill Patients With Coronavirus Disease 2019 (COVID-19)
This article has 14 authors:Reviewed by ScreenIT
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Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs
This article has 11 authors:Reviewed by ScreenIT