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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Visualization of SARS-CoV-2 Infection Scenes by ‘Zero-Shot’ Enhancements of Electron Microscopy Images
This article has 3 authors:Reviewed by ScreenIT
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Prospective Predictive Performance Comparison between Clinical Gestalt and Validated COVID-19 Mortality Scores
This article has 11 authors:Reviewed by ScreenIT
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Effect of COVID-19 on Lipid Profile and its Correlation with Acute Phase Reactants
This article has 9 authors:Reviewed by ScreenIT
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Survey of Behaviour Attitudes Towards Preventive Measures Following COVID-19 Vaccination
This article has 8 authors:Reviewed by ScreenIT
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Increased Serum Thromboxane A2 and Prostacyclin but Lower Complement C3 and C4 Levels in COVID-19: Associations with Chest CT Scan Anomalies and Lowered Peripheral Oxygen Saturation
This article has 4 authors:Reviewed by ScreenIT
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Forecasting COVID-19 Disease Cases Using the SARIMA-NNAR Hybrid Model
This article has 2 authors:Reviewed by ScreenIT
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Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2
This article has 18 authors:Reviewed by ScreenIT
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Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study
This article has 15 authors:Reviewed by ScreenIT
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An Epidemiological Study to Investigate Links between Atmospheric Pollution from Farming and SARS-CoV-2 Mortality
This article has 16 authors:Reviewed by ScreenIT
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Mass Spectrometric Identification of SARS-CoV-2 Proteins from Gargle Solution Samples of COVID-19 Patients
This article has 7 authors:Reviewed by ScreenIT