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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Cross-sectional Assessment of COVID-19 Vaccine Acceptance Among Health Care Workers in Los Angeles
This article has 8 authors:Reviewed by ScreenIT
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COVID-19 positivity associated with traumatic stress response to childbirth and no visitors and infant separation in the hospital
This article has 5 authors:Reviewed by ScreenIT
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Using the COVID-19 to influenza ratio to estimate the numbers of symptomatic COVID-19 cases in Wuhan prior to the lockdown
This article has 3 authors:Reviewed by ScreenIT
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Optimising SARS-CoV-2 pooled testing strategies on social networks for low-resource settings
This article has 3 authors:Reviewed by ScreenIT
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State-of-the-Art Risk Models for Diabetes, Hypertension, Visual Diminution, and COVID-19 Severity in Mexico
This article has 5 authors:Reviewed by ScreenIT
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Utilize State Transition Matrix Model to Predict the Novel Corona Virus Infection Peak and Patient Distribution
This article has 3 authors:Reviewed by ScreenIT
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Markers Of Coagulation And Hemostatic Activation Identify COVID-19 Patients At High Risk For Thrombotic Events, ICU Admission and Intubation
This article has 13 authors:Reviewed by ScreenIT
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The association between ABO blood group and SARS-CoV-2 infection: A meta-analysis
This article has 4 authors:Reviewed by ScreenIT
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Superspreaders and high variance infectious diseases
This article has 3 authors:Reviewed by ScreenIT
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Automated Western immunoblotting detection of anti-SARS-CoV-2 serum antibodies
This article has 9 authors:Reviewed by ScreenIT