ArticleJournal of the American Medical Informatics Association : JAMIA2019
Identifying translational science through embeddings of controlled vocabularies.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed.
- Steps toward developing an algorithm to facilitate recognition of translational science.Journal of clinical and translational science · 2026Article
- Most researchers would receive more recognition if assessed by article-level metrics than by journal-level metrics.PLoS biology · 2025Article
- The landscape of biomedical research.Patterns (New York, N.Y.) · 2024Article
- Distinguishing between translational science and translational research in CTSA pilot studies: A collaborative project across 12 CTSA hubs.Journal of clinical and translational science · 2024Article
- Methods for identifying biomedical translation: a systematic review.American journal of translational research · 2022Review
- Measuring the Interactions Between Health Demand, Informatics Supply, and Technological Applications in Digital Medical Innovation for China: Content Mapping and Analysis.JMIR medical informatics · 2021Article
- Predicting translational progress in biomedical research.PLoS biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTranslational science aims at "translating" basic scientific discoveries into clinical applications. The identification of translational science has practicality such as evaluating the effectiveness of investments made into large programs like the Clinical and Translational Science Awards. Despite several proposed methods that group publications-the primary unit of research output-into some categories, we still lack a quantitative way to place articles onto the full, continuous spectrum from basic research to clinical medicine. MATERIALS AND
methodsI learn vector representations of controlled vocabularies assigned to Medline articles to obtain a translational axis that points from basic science to clinical medicine. The projected position of a term on the translational axis, expressed by a continuous quantity, indicates the term's "appliedness." The position of an article, determined by the average location over its terms, quantifies the degree of its appliedness, which I term the level score.
resultsI validate the present method by comparing with previous techniques, showing excellent agreement yet uncovering significant variations of scores of articles in previously defined categories. The measure allows us to characterize the standing of journals, disciplines, and the entire biomedical literature along the basic-applied spectrum. Analysis on large-scale citation network reveals 2 main findings. First, direct citations mainly occurred between articles with similar scores. Second, shortest paths are more likely ended up with an article closer to the basic end of the spectrum, regardless of where the starting article is on the spectrum.
conclusionsThe proposed method provides a quantitative way to identify translational science.
Indexed as
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Registered trials
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.