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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Modelling patterns of SARS-CoV-2 circulation in the Netherlands, August 2020-February 2022, revealed by a nationwide sewage surveillance program
This article has 13 authors:Reviewed by ScreenIT
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Estimated Global Proportions of Individuals With Persistent Fatigue, Cognitive, and Respiratory Symptom Clusters Following Symptomatic COVID-19 in 2020 and 2021
This article has 128 authors:Reviewed by ScreenIT
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SARS-CoV-2 variants in the making: Sequential intrahost evolution and forward transmissions in the context of persistent infections
This article has 29 authors:Reviewed by ScreenIT
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Outpatient regimens to reduce COVID-19 hospitalisations: a systematic review and meta-analysis of randomized controlled trials
This article has 8 authors:Reviewed by ScreenIT
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Comparison of the burnout among medical residents before and during the pandemic
This article has 6 authors:Reviewed by ScreenIT
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Comparison of a Target Trial Emulation Framework vs Cox Regression to Estimate the Association of Corticosteroids With COVID-19 Mortality
This article has 7 authors:Reviewed by ScreenIT
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The connection between COVID-19 vaccine abundance, vaccination coverage, and public trust in government across the globe
This article has 1 author:Reviewed by ScreenIT
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Uncertainty Quantification in COVID-19 Detection Using Evidential Deep Learning
This article has 3 authors:Reviewed by ScreenIT
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Tixagevimab/Cilgavimab for Prevention of COVID-19 during the Omicron Surge: Retrospective Analysis of National VA Electronic Data
This article has 10 authors:Reviewed by ScreenIT
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Identifying COVID-19 phenotypes using cluster analysis and assessing their clinical outcomes
This article has 8 authors:Reviewed by ScreenIT