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
-
Assisting scalable diagnosis automatically via CT images in the combat against COVID-19
This article has 45 authors:Reviewed by ScreenIT
-
Impact of COVID-19 Infection on Maternal and Neonatal Outcomes: A Review of 11078 Pregnancies Reported in the Literature
This article has 2 authors:Reviewed by ScreenIT
-
Upregulation of Human Endogenous Retroviruses in Bronchoalveolar Lavage Fluid of COVID-19 Patients
This article has 14 authors:Reviewed by ScreenIT
-
Comorbidities and Disparities in Outcomes of COVID-19 Among African American and White Patients
This article has 3 authors:Reviewed by ScreenIT
-
Screening of healthcare workers for SARS-CoV-2 highlights the role of asymptomatic carriage in COVID-19 transmission
This article has 27 authors:Reviewed by ScreenIT
-
Preliminary evaluation of the safety and efficacy of oral human antimicrobial peptide LL-37 in the treatment of patients of COVID-19, a small-scale, single-arm, exploratory safety study
This article has 27 authors:Reviewed by ScreenIT
-
Nebulized in-line endotracheal dornase alfa and albuterol administered to mechanically ventilated COVID-19 patients: a case series
This article has 5 authors:Reviewed by ScreenIT
-
Saliva is less sensitive than nasopharyngeal swabs for COVID-19 detection in the community setting
This article has 11 authors:Reviewed by ScreenIT
-
This article has 3 authors:
Reviewed by ScreenIT
-
COVID-19 and Environmental Factors. A PRISMA-Compliant Systematic Review
This article has 4 authors:Reviewed by ScreenIT