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
-
Strategies to reduce the risk of SARS-CoV-2 importation from international travellers: modelling estimations for the United Kingdom, July 2020
This article has 11 authors:Reviewed by ScreenIT
-
Effect of hydroxychloroquine with or without azithromycin on the mortality of coronavirus disease 2019 (COVID-19) patients: a systematic review and meta-analysis
This article has 6 authors:Reviewed by ScreenIT
-
Sequential Vaccination for Containing Epidemics
This article has 3 authors:Reviewed by ScreenIT
-
Efficacy of chloroquine and hydroxychloroquine in treating COVID-19 infection: A meta-review of systematic reviews and an updated meta-analysis
This article has 11 authors:Reviewed by ScreenIT
-
Transmission dynamics and control of COVID-19 in Chile, March-October, 2020
This article has 7 authors:Reviewed by ScreenIT
-
National Longitudinal Mediators of Psychological Distress During Stringent COVID-19 Lockdown
This article has 15 authors:Reviewed by ScreenIT
-
A model of workflow in the hospital during a pandemic to assist management
This article has 4 authors:Reviewed by ScreenIT
-
The effect of face mask mandates during the COVID-19 pandemic on the rate of mask use in the United States
This article has 1 author: -
Pitting the Gumbel and logistic growth models against one another to model COVID-19 spread
This article has 3 authors:Reviewed by ScreenIT
-
Epidemiological and clinical characteristics of COVID-19 in Brazil using digital technology
This article has 6 authors:Reviewed by ScreenIT