title: Identifying translational science within the triangle of biomedicine.
abstract: BACKGROUND
  The National Institutes of Health (NIH) Roadmap places special emphasis on "bench-to-bedside" research, or the "translation" of basic science research into practical clinical applications. The Clinical and Translational Science Awards (CTSA) Consortium is one example of the large investments being made to develop a national infrastructure to support translational science, which involves reducing regulatory burdens, launching new educational initiatives, and forming partnerships between academia and industry. However, while numerous definitions have been suggested for translational science, including the qualitative T1-T4 classification, a consensus has not yet been reached. This makes it challenging to tract the impact of these major policy changes.
  
  
  METHODS
  In this study, we use a bibliometric approach to map PubMed articles onto a graph, called the Triangle of Biomedicine. The corners of the triangle represent research related to animals, cells and molecules, and humans; and, the position of a publication on the graph is based on its topics, as determined by its Medical Subject Headings (MeSH). We define translation as movement of a collection of articles, or the articles that cite those articles, towards the human corner.
  
  
  RESULTS
  The Triangle of Biomedicine provides a quantitative way of determining if an individual scientist, research organization, funding agency, or scientific field is producing results that are relevant to clinical medicine. We validate our technique using examples that have been previously described in the literature and by comparing it to prior methods of measuring translational science.
  
  
  CONCLUSIONS
  The Triangle of Biomedicine is a novel way to identify translational science and track changes over time. This is important to policy makers in evaluating the impact of the large investments being made to accelerate translation. The Triangle of Biomedicine also provides a simple visual way of depicting this impact, which can be far more powerful than numbers alone.
authors.affiliation: Harvard Medical School, Boston, MA, USA. weber@hms.harvard.edu
authors.forename: Griffin M
authors.initials: GM
authors.lastname: Weber
mesh_terms: D000465:Algorithms; D015706:Bibliometrics; D035843:Biomedical Research / Q000379:methods; D006801:Humans; D046650:Medical Subject Headings; D008962:Models, Theoretical; D009316:National Institutes of Health (U.S.); D039781:PubMed; D011640:Public Policy; D012586:Science / Q000639:trends; D009626:Terminology as Topic; D057170:Translational Research, Biomedical / Q000379:methods* / Q000592:standards; D014481:United States