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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Hijacking of Cellular Functions by Severe Acute Respiratory Syndrome Coronavirus-2. Permeabilization and Polarization of the Host Lipid Membrane by Viroporins
This article has 3 authors:Reviewed by ScreenIT
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The incidence-based dynamic reproduction index: accurate determination, diagnostic sensitivity, and predictive power
This article has 2 authors:Reviewed by ScreenIT
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mRNA-1273 but not BNT162b2 induces antibodies against polyethylene glycol (PEG) contained in mRNA-based vaccine formulations
This article has 13 authors:Reviewed by ScreenIT
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Safety and effectiveness of RBD-specific polyclonal equine F(ab´)2 fragments for the treatment of hospitalized patients with severe Covid-19 disease: A retrospective cohort study
This article has 28 authors:Reviewed by ScreenIT
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Vaccine Effectiveness of CanSino (Adv5-nCoV) Coronavirus Disease 2019 (COVID-19) Vaccine Among Childcare Workers—Mexico, March–December 2021
This article has 11 authors:Reviewed by ScreenIT
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Development of a Prediction Model for the Management of Noncommunicable Diseases Among Older Syrian Refugees Amidst the COVID-19 Pandemic in Lebanon
This article has 10 authors:Reviewed by ScreenIT
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Can We Really Trust the Findings of the COVID-19 Research? Quality Assessment of Randomized Controlled Trials Published on COVID-19
This article has 4 authors:Reviewed by ScreenIT
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Using a real-world network to model the tradeoff between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics
This article has 3 authors:Reviewed by ScreenIT
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The effect of antidepressants on the severity of COVID-19 in hospitalized patients: A systematic review and meta-analysis
This article has 6 authors:Reviewed by ScreenIT
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COVID-19 relevant genetic variants confirmed in an admixed population
This article has 16 authors:Reviewed by ScreenIT