more info


The best way to reference the trial2rev system and the data source:

  • P Newman, R Bashir, D Surian, FT Bourgeois, AG Dunn (2018) trial2rev: combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews, JAMIA Open. doi:10.1093/jamiaopen/ooy062;

Other related articles:

  • D Surian, AG Dunn, L Orenstein, R Bashir, E Coiera, FT Bourgeois (2018) A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates, Journal of Biomedical Informatics, 79:32-40. doi:10.1016/j.jbi.2018.01.008; preprint: arXiv: 1709.06758.
  • AG Dunn, E Coiera, FT Bourgeois (2018) Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov, Journal of Clinical Epidemiology, 95:94-101. doi: 10.1016/j.jclinepi.2017.12.007; preprint: arXiv:1709.02116.
  • R Bashir, FT Bourgeois, AG Dunn (2017) A systematic review of the processes used to link clinical trial registrations to their published results, Systematic Reviews, 6:123. doi: 10.1186/s13643-017-0518-3.
  • R Bashir, AG Dunn (2016) A systematic review protocol assessing the processes for linking clinical trial registries and their published results, BMJ Open, 6(10):e013048. doi: 10.1136/bmjopen-2016-013048.

Data sources used in the trial2rev system include CrossRef, PubMed/MEDLINE, and ClinicalTrials.gov. Additional sources of data for linking systematic reviews and trials may be added in the future.

The research was supported by a grant from the Agency for Healthcare Research and Quality (R03HS024798) and the National Library of Medicine of the National Institutes of Health (R01LM012976), with investigators Florence Bourgeois, Adam Dunn (adam.dunn@mq.edu.au), and Kenneth Mandl. We also receive support from the AWS Cloud Credits for Research program. This website and the underlying systems were developed by Paige Martin (paige.newman@mq.edu.au).

Affiliations for investigators and developers include the Computational Health Informatics Program, Boston Children’s Hospital; Department of Pediatrics, Harvard Medical School; Department of Biomedical Informatics, Harvard Medical School; and the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University.