Modeling the dissemination and uptake of clinical trials results

Rosas, Scott R.; Cope, Marie T.; Kagan, Jonathan

doi:10.1093/reseval/rvt005

Cited by 14 publications

(13 citation statements)

References 22 publications

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“…In addition, a recent detailed bibliometric analysis suggests the rapid dissemination of clinical findings. 36 Thus, it is not surprising that most of the T100 articles (58%) in the present study are clinical research, consistent with analyses in other fields. 37–39 The mean citation number per clinical research article was higher than that of basic research articles (404 vs 328).…”

Section: Discussionsupporting

confidence: 86%

Hundred top-cited articles focusing on acute kidney injury: a bibliometric analysis

et al. 2016

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BackgroundAcute kidney injury (AKI) is a major global health issue, associated with poor short-term and long-term outcomes. Research on AKI is increasing with numerous articles published. However, the quantity and quality of research production in the field of AKI is unclear.Methods and analysisTo analyse the characteristics of the most cited articles on AKI and to provide information about achievements and developments in AKI, we searched the Science Citation Index Expanded for citations of AKI articles. For the top 100 most frequently cited articles (T100), we evaluated the number of citations, publication time, province of origin, journal, impact factor, topic or subspecialty of the research, and publication type.ResultsThe T100 articles ranged from a maximum of 1971 citations to a minimum of 215 citations (median 302 citations). T100 articles were published from 1951 to 2011, with most articles published in the 2000s (n=77), especially the 5-year period from 2002 to 2006 (n=51). The publications appeared in 30 journals, predominantly in the general medical journals, led by New England Journal of Medicine (n=17), followed by expert medical journals, led by the Journal of the American Society of Nephrology (n=16) and Kidney International (n=16). The majority (83.7%) of T100 articles were published by teams involving ≥3 authors. T100 articles originated from 15 countries, led by the USA (n=81) followed by Italy (n=9). Among the T100 articles, 69 were clinical research, 25 were basic science, 21 were reviews, 5 were meta-analyses and 3 were clinical guidelines. Most clinical articles (55%) included patients with any cause of AKI, followed by the specific causes of contrast-induced AKI (25%) and cardiac surgery-induced AKI (15%).ConclusionsThis study provides a historical perspective on the scientific progress on AKI, and highlights areas of research requiring further investigations and developments.

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Section: Discussionsupporting

confidence: 86%

Hundred top-cited articles focusing on acute kidney injury: a bibliometric analysis

et al. 2016

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“…In addition, a recently published detailed bibliometric analysis suggests that the dissemination of clinical findings is very rapid. 9 Our limited survey, based on the analysis to identify the citation source for the top three T100 clinical studies, found that most of their citations (2/3) came from other original articles (both clinical and preclinical studies) with the rest of citations (1/3) being found in subsequent reviews, editorials, or meta-analyses. This distribution suggests that conclusions of these highly cited clinical studies had stimulated much subsequent original research.…”

Section: Resultsmentioning

confidence: 99%

Anatomy of Success

Galis

2014

Hypertension

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“…Approaches to categorization of publications often applied filters based on title words [ 16 – 18 ]. There have also been efforts to look at citation usage, or knowledge transfer, across categories [ 16 , 19 , 20 ]. More recently, Weber proposed a triangle of biomedicine [ 21 ] where articles are mapped to either humans, animals, or cells and molecules based on the medical subject headings (MeSH) used by PubMed.…”

Section: Introductionmentioning

confidence: 99%

Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach

Surkis¹,

Hogle

DiazGranados

et al. 2016

J Transl Med

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BackgroundTranslational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications.MethodsBased on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier.ResultsThe definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4.ConclusionsThe combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-016-0992-8) contains supplementary material, which is available to authorized users.

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Modeling the dissemination and uptake of clinical trials results

Cited by 14 publications

References 22 publications

Hundred top-cited articles focusing on acute kidney injury: a bibliometric analysis

Hundred top-cited articles focusing on acute kidney injury: a bibliometric analysis

Anatomy of Success

Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach

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