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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Inflammasome-related Markers upon ICU Admission do not Correlate with Outcome in Critically Ill COVID-19 Patients
This article has 7 authors:Reviewed by ScreenIT
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Safety and efficacy of the two doses conjugated protein-based SOBERANA-02 COVID-19 vaccine and of a heterologous three-dose combination with SOBERANA-Plus: a double-blind, randomised, placebo-controlled phase 3 clinical trial
This article has 34 authors:Reviewed by ScreenIT
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Author Correction: Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals
This article has 41 authors:Reviewed by Rapid Reviews Infectious Diseases, ScreenIT
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Azithromycin consumption during the COVID-19 pandemic in Croatia, 2020
This article has 5 authors:Reviewed by ScreenIT
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IFI27 transcription is an early predictor for COVID-19 outcomes, a multi-cohort observational study
This article has 60 authors:Reviewed by ScreenIT
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Emergence and spread of a sub-lineage of SARS-CoV-2 Alpha variant B.1.1.7 in Europe, and with further evolution of spike mutation accumulations shared with the Beta and Gamma variants
This article has 10 authors:Reviewed by ScreenIT
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COVID-19 vaccine brand hesitancy and other challenges to vaccination in the Philippines
This article has 4 authors:Reviewed by ScreenIT
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Correlates of Coronavirus Disease 2019 (COVID-19) Vaccine Hesitancy Among People Who Inject Drugs in the San Diego-Tijuana Border Region
This article has 9 authors:Reviewed by ScreenIT
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Multiethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes
This article has 8 authors:Reviewed by ScreenIT
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An objective framework for evaluating unrecognized bias in medical AI models predicting COVID-19 outcomes
This article has 7 authors:Reviewed by ScreenIT