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
-
The Relationship Between Age and Mental Health Among Adults in Iran During the COVID-19 Pandemic
This article has 7 authors:Reviewed by ScreenIT
-
COVID‐19 and children with cancer: Parents’ experiences, anxieties and support needs
This article has 7 authors:Reviewed by ScreenIT
-
Performance and health economic evaluation of the Mount Sinai COVID-19 serological assay identifies modification of thresholding as necessary to maximise specificity of the assay
This article has 18 authors:Reviewed by ScreenIT
-
Emotional health concerns of oncology physicians in the United States: Fallout during the COVID-19 pandemic
This article has 10 authors:Reviewed by ScreenIT
-
Coronavirus and birth in Italy: results of a national population-based cohort study
This article has 5 authors:Reviewed by ScreenIT
-
Sensitivity of anti-SARS-CoV-2 serological assays in a high-prevalence setting
This article has 19 authors:Reviewed by ScreenIT
-
Temporal Clinical and Laboratory Response to Interleukin-6 Receptor Blockade With Tocilizumab in 89 Hospitalized Patients With COVID-19 Pneumonia
This article has 9 authors:Reviewed by ScreenIT
-
The prognostic value of comorbidity for the severity of COVID-19: A systematic review and meta-analysis study
This article has 7 authors:Reviewed by ScreenIT
-
Estimating the impact of physical distancing measures in containing COVID-19: an empirical analysis
This article has 3 authors:Reviewed by ScreenIT
-
Mortality Attributed to COVID-19 in High-Altitude Populations
This article has 2 authors:Reviewed by ScreenIT