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
-
Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays
This article has 4 authors:Reviewed by ScreenIT
-
Public attitudes towards COVID‐19 contact tracing apps: A UK‐based focus group study
This article has 4 authors:Reviewed by ScreenIT
-
Mixed Chinese herbs and Western medicine for novel coronavirus disease 2019 (COVID-19): a mixed method review
This article has 6 authors:Reviewed by ScreenIT
-
Higher body mass index is an important risk factor in COVID-19 patients: a systematic review and meta-analysis
This article has 5 authors:Reviewed by ScreenIT
-
Proteins associated with neutrophil degranulation are upregulated in nasopharyngeal swabs from SARS-CoV-2 patients
This article has 9 authors:Reviewed by ScreenIT
-
The effect of ambient temperature on worldwide COVID-19 cases and deaths – an epidemiological study
This article has 6 authors:Reviewed by ScreenIT
-
The impact of testing and infection prevention and control strategies on within-hospital transmission dynamics of COVID-19 in English hospitals
This article has 8 authors:Reviewed by ScreenIT
-
Subnational analysis of the COVID-19 epidemic in Brazil
This article has 52 authors:Reviewed by ScreenIT
-
Current Evidence of the Pharmacological Treatments for Novel Coronavirus Disease 2019 (COVID-19) A Scoping Review
This article has 5 authors:Reviewed by ScreenIT
-
Sarilumab use in severe SARS-CoV-2 pneumonia
This article has 41 authors:Reviewed by ScreenIT