This comprehensive assessment of risk factors can help support clinicians in reducing the incidence of SIM in their patient population on statins.
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.
Although many individual studies have reported non-significant findings in comparisons of VM and OM, the enhanced power afforded by meta-analysis revealed that the pedagogical approach of VM is modestly superior to that of OM and is preferred by learners.
In academia, authorship is considered a currency, and is important for career advancement. As the Journal of Bone and Mineral Research (JBMR®) is the highest-ranked journal in the field of bone, muscle, and mineral metabolism, and is the official publication of the American Society for Bone and Mineral Research, we sought to examine authorship changes over JBMR®’s 30-year history. Two bibliometric methods were used to collect the data. The “decade method” included all published manuscripts throughout one year in each decade over the past 30 years starting with the inaugural year, yielding 746 manuscripts for analysis. The “random method” examined 10% of published manuscripts from each of the 30 years, yielding 652 manuscripts for analysis. Using both methods, the average number of authors per manuscript, numerical location of the corresponding author, number of collaborating institutions, number of collaborating countries, number of printed manuscript pages, and the number of times each manuscript was cited all significantly increased between 1986 and 2015 (p < 10−4). Using the decade method, there was a significant increase in the percentage of female first authors over time from 35.8% in 1986 to 47.7% in 2015 (p = 0.02) and this trend was confirmed using the random method. The highest percentage of female first authors in 2015 was in Europe (60.0%), and Europe also had the most dramatic increase in female first authors over time (more than double in 2015 compared with 1986). However, the overall number of female corresponding authors did not significantly change during the past 30 years. With the increasing demands of publishing in academic medicine, understanding changes in publishing characteristics over time and by geographical region is important. These findings highlight JBMR®’s authorship trends over the past 30 years, demonstrate those countries having the most changes, and where challenges still exist.
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