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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Therapeutic Anticoagulation in Non-Critically Ill Patients with Covid-19
This article has 37 authors:Reviewed by ScreenIT
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“How Do We Do This at a Distance?!” A Descriptive Study of Remote Undergraduate Research Programs during COVID-19
This article has 44 authors:Reviewed by ScreenIT
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Reduction in hospitalised COPD exacerbations during COVID-19: A systematic review and meta-analysis
This article has 8 authors:Reviewed by ScreenIT
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Toxoplasmosis is a risk factor for acquiring SARS-CoV-2 infection and a severe course of COVID-19 in the Czech and Slovak population: a preregistered exploratory internet cross-sectional study
This article has 1 author:Reviewed by ScreenIT
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Predicting COVID-19 Transmission to Inform the Management of Mass Events: Model-Based Approach
This article has 8 authors:Reviewed by ScreenIT
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Performance Evaluation of the BD SARS-CoV-2 Reagents for the BD MAX System
This article has 14 authors:Reviewed by ScreenIT
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Surveillance of COVID-19 Vaccination in Nursing Homes, United States, December 2020–July 2021
This article has 11 authors:Reviewed by ScreenIT
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Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework
This article has 12 authors:Reviewed by ScreenIT
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SARS‐CoV‐2 R.1 lineage variants that prevailed in Tokyo in March 2021
This article has 15 authors:Reviewed by ScreenIT
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Host transcriptional response to SARS‐CoV‐2 infection in COVID‐19 patients
This article has 16 authors:Reviewed by ScreenIT