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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Vaccination is Australia's most important COVID-19 public health action, even though herd immunity is unlikely
This article has 10 authors:Reviewed by ScreenIT
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SARS-CoV-2 Seroprevalence before Delta Variant Surge, Chattogram, Bangladesh, March–June 2021
This article has 15 authors:Reviewed by ScreenIT
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Evaluating discharges and readmissions using a COVID Virtual Ward model: a retrospective data study assessing patient outcomes and the likely staffing commitment
This article has 8 authors:Reviewed by ScreenIT
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Respiratory mucosal delivery of next-generation COVID-19 vaccine provides robust protection against both ancestral and variant strains of SARS-CoV-2
This article has 29 authors:Reviewed by ScreenIT
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Prevalence of anti–platelet factor 4/polyanionic antibodies after COVID‐19 vaccination with ChAdOx1 nCoV‐19 and CoronaVac in Thais
This article has 9 authors:Reviewed by ScreenIT
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Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data
This article has 4 authors:Reviewed by ScreenIT
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Post-COVID syndrome. A case series and comprehensive review
This article has 12 authors:Reviewed by ScreenIT
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Key Substitutions in the Spike Protein of SARS-CoV-2 Variants Can Predict Resistance to Monoclonal Antibodies, but Other Substitutions Can Modify the Effects
This article has 9 authors:Reviewed by ScreenIT
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Platelet size as a predictor for severity and mortality in COVID-19 patients: a systematic review and meta-analysis
This article has 3 authors:Reviewed by ScreenIT
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Scaling SARS-CoV-2 wastewater concentrations to population estimates of infection
This article has 4 authors:Reviewed by ScreenIT