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
-
Disease severity dictates SARS-CoV-2-specific neutralizing antibody responses in COVID-19
This article has 22 authors:Reviewed by ScreenIT
-
The risk of indoor sports and culture events for the transmission of COVID-19
This article has 11 authors:Reviewed by ScreenIT
-
Protective face mask filter capable of inactivating SARS-CoV-2, and methicillin-resistant Staphylococcus aureus and Staphylococcus epidermidis
This article has 7 authors:Reviewed by ScreenIT
-
Secondary attack rate in household contacts of COVID-19 Paediatric index cases: a study from Western India
This article has 3 authors:Reviewed by ScreenIT
-
Diagnosis value of SARS‐CoV‐2 antigen/antibody combined testing using rapid diagnostic tests at hospital admission
This article has 15 authors:Reviewed by ScreenIT
-
Experience from a COVID-19 screening centre of a tertiary care institution
This article has 6 authors:Reviewed by ScreenIT
-
In Vitro Safety “Clinical Trial” of the Cardiac Liability of Hydroxychloroquine and Azithromycin as COVID19 Polytherapy
This article has 7 authors:Reviewed by ScreenIT
-
Mental health of undocumented college students during the COVID-19 pandemic
This article has 4 authors:Reviewed by ScreenIT
-
LungAI: A Deep Learning Convolutional Neural Network for Automated Detection of COVID-19 from Posteroanterior Chest X-Rays
This article has 1 author:Reviewed by ScreenIT
-
Influence of malaria endemicity and tuberculosis prevalence on COVID-19 mortality
This article has 1 author:Reviewed by ScreenIT