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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Narrowing gap in regional and age-specific excess mortality in the first year and a half of COVID-19 in Hungary
This article has 1 author:Reviewed by ScreenIT
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Exploring the impact of shielding advice on the health and wellbeing of individuals identified as extremely vulnerable and advised to shield in Southwest England amid the COVID-19 pandemic: A mixed-methods evaluation
This article has 15 authors:Reviewed by ScreenIT
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Evaluation of commercial Anti-SARS-CoV-2 neutralizing antibody assays in seropositive subjects
This article has 14 authors:Reviewed by ScreenIT
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Timing of Breakthrough Infection Risk After Vaccination Against SARS-CoV-2
This article has 3 authors:Reviewed by ScreenIT
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Lineage replacement and evolution captured by three years of the United Kingdom Covid Infection Survey
This article has 34 authors:Reviewed by ScreenIT
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COVID-19-associated hospitalizations among children less than 12 years of age in the United States
This article has 10 authors:Reviewed by ScreenIT
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Validation of a Novel Fluorescent Lateral Flow Assay for Rapid Qualitative and Quantitative Assessment of Total Anti-SARS-CoV-2 S-RBD Binding Antibody Units (BAU) from Plasma or Fingerstick Whole-Blood of COVID-19 Vaccinees
This article has 21 authors:Reviewed by ScreenIT
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Meta-analysis of COVID-19 patients to understand the key predictors of mortality in the non-vaccinated groups in remote settings
This article has 6 authors:Reviewed by ScreenIT
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Effects of Varying Approaches to Lifting COVID-19 Pandemic Restrictions in the United States
This article has 5 authors:Reviewed by ScreenIT
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Development of a One-Step Qualitative RT-PCR Assay to Detect the SARS-CoV-2 Omicron (B.1.1.529) Variant in Respiratory Specimens
This article has 7 authors:Reviewed by ScreenIT