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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Motivation, Intention and Action: Wearing Masks to Prevent the Spread of COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5
This article has 20 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Omicron BA.1 and BA.2 Neutralizing Activity Following Pre-Exposure Prophylaxis with Tixagevimab plus Cilgavimab in Vaccinated Solid Organ Transplant Recipients
This article has 24 authors:Reviewed by ScreenIT
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Overuse in US Medicare during the COVID-19 pandemic: 2020 versus 2019
This article has 4 authors:Reviewed by ScreenIT
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Noninvasive ventilation strategies for patients with severe or critical COVID-19: A rapid review of clinical outcomes
This article has 2 authors:Reviewed by ScreenIT
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Real-world effectiveness of molnupiravir and nirmatrelvir plus ritonavir against mortality, hospitalisation, and in-hospital outcomes among community-dwelling, ambulatory patients with confirmed SARS-CoV-2 infection during the omicron wave in Hong Kong: an observational study
This article has 6 authors:Reviewed by ScreenIT
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Long Covid stigma: Estimating burden and validating scale in a UK-based sample
This article has 6 authors:Reviewed by ScreenIT
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Limited induction of polyfunctional lung-resident memory T cells against SARS-CoV-2 by mRNA vaccination compared to infection
This article has 10 authors:Reviewed by ScreenIT
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Modelling the impacts of public health interventions and weather on SARS-CoV-2 Omicron outbreak in Hong Kong
This article has 2 authors:Reviewed by ScreenIT
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COVID-19 pandemic impact on preterm birth and stillbirth rates associated with socioeconomic disparities: A quasi-experimental study
This article has 14 authors:Reviewed by ScreenIT