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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A Machine Learning Explanation of Incidence Inequalities of SARS-CoV-2 Across 88 Days in 157 Countries
This article has 1 author:Reviewed by ScreenIT
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Laboratory findings in coronavirus disease 2019 (COVID-19) patients: a comprehensive systematic review and meta-analysis
This article has 4 authors:Reviewed by ScreenIT
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Predictors of mental health during the Covid-19 pandemic in the US: Role of economic concerns, health worries and social distancing
This article has 6 authors:Reviewed by ScreenIT
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A scaling approach to estimate the age-dependent COVID-19 infection fatality ratio from incomplete data
This article has 1 author:Reviewed by ScreenIT
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A how-to-guide to building a robust SARS-CoV-2 testing program at a university-based health system
This article has 35 authors:Reviewed by ScreenIT
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Clinical Evaluation of Self-Collected Saliva by Quantitative Reverse Transcription-PCR (RT-qPCR), Direct RT-qPCR, Reverse Transcription–Loop-Mediated Isothermal Amplification, and a Rapid Antigen Test To Diagnose COVID-19
This article has 14 authors:Reviewed by ScreenIT
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Knowledge and awareness-based survey of COVID-19 within the eye care profession in Nepal: Misinformation is hiding the truth
This article has 5 authors:Reviewed by ScreenIT
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Drug-drug interactions between COVID-19 treatments and antipsychotics drugs: integrated evidence from 4 databases and a systematic review
This article has 6 authors:Reviewed by ScreenIT
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Less Wrong COVID-19 Projections With Interactive Assumptions
This article has 12 authors:Reviewed by ScreenIT
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Studying the effect of lockdown using epidemiological modelling of COVID-19 and a quantum computational approach using the Ising spin interaction
This article has 6 authors:Reviewed by ScreenIT