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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Accuracy of telephone triage for predicting adverse outcomes in suspected COVID-19: an observational cohort study
This article has 15 authors:Reviewed by ScreenIT
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Clinical Profile and Immediate Outcome of Multisystem Inflammatory Syndrome in Children Associated with COVID-19
This article has 8 authors:Reviewed by ScreenIT
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Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury
This article has 16 authors:Reviewed by ScreenIT
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Engineered chimeric T cell receptor fusion construct (TRuC)-expressing T cells prevent translational shutdown in SARS-CoV-2-infected cells
This article has 12 authors:Reviewed by ScreenIT
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Ovarian granulosa cells from women with PCOS express low levels of SARS-CoV-2 receptors and co-factors
This article has 5 authors:Reviewed by ScreenIT
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Coverage and Estimated Effectiveness of mRNA COVID-19 Vaccines Among US Veterans
This article has 7 authors:Reviewed by ScreenIT
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The mental health of NHS staff during the COVID-19 pandemic: two-wave Scottish cohort study
This article has 11 authors:Reviewed by ScreenIT
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COVID-19 Vaccine Coverage Index: Identifying barriers to COVID-19 vaccine uptake across U.S. counties
This article has 5 authors:Reviewed by ScreenIT
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Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy
This article has 2 authors:Reviewed by ScreenIT
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Assessment of COVID-19 vaccine hesitancy among Zimbabweans: A rapid national survey
This article has 5 authors:Reviewed by ScreenIT