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
-
INFERRED RESOLUTION THROUGH HERD IMMMUNITY OF FIRST COVID-19 WAVE IN MANAUS, BRAZILIAN AMAZON
This article has 10 authors:Reviewed by ScreenIT
-
Test sensitivity is secondary to frequency and turnaround time for COVID-19 screening
This article has 9 authors:Reviewed by ScreenIT
-
Now-casting the COVID-19 epidemic: The use case of Japan, March 2020
This article has 3 authors:Reviewed by ScreenIT
-
Trends and clinical characteristics of COVID-19 vaccine recipients: a federated analysis of 57.9 million patients' primary care records in situ using OpenSAFELY
This article has 46 authors:Reviewed by ScreenIT
-
Relationship of SARS-CoV-2–specific CD4 response to COVID-19 severity and impact of HIV-1 and tuberculosis coinfection
This article has 14 authors:Reviewed by ScreenIT
-
Convalescent Plasma in COVID-19. Mortality-Safety First Results of the Prospective Multicenter FALP 001-2020 Trial
This article has 31 authors:Reviewed by ScreenIT
-
Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States
This article has 42 authors:Reviewed by ScreenIT
-
Pentoxifylline and Covid-19: A Systematic Review
This article has 3 authors:Reviewed by ScreenIT
-
Patient outcomes after hospitalisation with COVID-19 and implications for follow-up: results from a prospective UK cohort
This article has 18 authors:Reviewed by ScreenIT
-
Characteristics and Evolution of COVID-19 Cases in Brazil: Mathematical Modeling and Simulation
This article has 4 authors:Reviewed by ScreenIT