Evidence map›Paper›PMID 23705970›Full record

ArticleJournal of translational medicine2013

Identifying translational science within the triangle of biomedicine.

Griffin M Weber

Abstract read
In one paragraph

Article in Journal of translational medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Article
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  7. Predicting substantive biomedical citations without full text.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  8. Article
  9. Article
  10. Review
  11. Methods for identifying biomedical translation: a systematic review.American journal of translational research · 2022
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Identifying translational science through embeddings of controlled vocabularies.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Griffin M WeberHarvard Medical School, Boston, MA, USA. weber@hms.harvard.edu

Funding

HARVARD CLINICAL AND TRANSLATIONAL SCIENCE CENTER (UL1)UL1RR025758 · NCRR · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2008 to 2011
$91.4M
NCRR NIH HHS UL1 RR 025758NCRR NIH HHS UL1 RR025758
6 · The paper itself

Abstract

backgroundThe 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.

methodsIn 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

AlgorithmsBibliometricsBiomedical ResearchHumansMedical Subject HeadingsModels, TheoreticalNational Institutes of Health (U.S.)Public PolicyPubMedScienceTerminology as TopicTranslational Research, BiomedicalUnited States

Identifiers

PMID23705970
PMCPMC3666890

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.