The authors of this article analyzed the differences in output when searching MEDLINE direct and MEDLINE via citation management software, EndNote X1, EndNote Web, and RefWorks. Several searches were performed on Ovid MEDLINE and PubMed directly. These searches were compared against the same searches conducted in Ovid MEDLINE and PubMed using the search features in EndNote X1, EndNote Web, and RefWorks. Findings indicated that for in-depth research users, should search the databases directly rather than through the citation management software interface. The search results indicated it would be appropriate to search databases via citation management software for citation verification tasks and for cursory keyword searching.
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http://dx.doi.org/10.1080/02763860802198804 | DOI Listing |
Front Oncol
January 2025
Department of Nursing, Jinjiang Municipal Hospital, Quanzhou, China.
Objective: To use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers.
Methods: The core set of the Web of Science database was searched for articles from 1994 to 2024. The R package "Bibliometrix" was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords.
Am J Cardiovasc Dis
December 2024
Cardiac Primary Prevention Research Center, Tehran Heart Center, Tehran University of Medical Sciences Tehran, Iran.
Objectives: To our knowledge, there is no clear consensus on a definitive cardiac rehabilitation method for patients undergoing Coronary Artery Bypass Graft (CABG). We conducted this systematic review to compare and evaluate the effects of two of the most frequent cardiac rehabilitation modalities, high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT), on cardiopulmonary variables.
Methods: We carried out a systematic search of the databases PubMed, Web of Science, Embase, Scopus, and Google Scholar.
Heliyon
January 2025
Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Aims: The study delved into the identification of key research areas and evolving trends within the domain of Enhanced External Counterpulsation, aiming to gain comprehensive insights into the subject matter.
Methods: Utilizing the sophisticated search parameter of 'topic' (TS) on the Web of Science (WoS) database, the necessary information was retrieved. This research employed an array of tools for effective data extraction, analysis, and visualization, which included Microsoft Excel for tabular management, HistCite Pro for citation analysis, GunnMap for geographical mapping, BibExcel for bibliometric assessments, and VOSviewer for network visualization purposes.
J Patient Exp
January 2025
Faculty of Health Sciences, School of Nursing, McMaster University, Hamilton, Canada.
Diabetes registries have grown in prevalence and incorporated patient engagement opportunities to support diabetes management. We aimed to understand the goals, purpose, and context for diabetes registries defined as patient-focused and how people with diabetes are engaging with these registries. We searched Pubmed, MEDLINE, Embase, and Emcare using the following criteria: (1) the population is people with diabetes mellitus, including type 1, type 2, and/or gestational diabetes; and (2) the study describes a patient focused registry.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Ophthalmology, the Fourth Affiliated Hospital of China Medical University, Shenyang, China.
Background: Recently, deep learning has become a popular area of research, and has revolutionized the diagnosis and prediction of ocular diseases, especially fundus diseases. This study aimed to conduct a bibliometric analysis of deep learning in the field of ophthalmology to describe international research trends and examine the current research directions.
Methods: This cross-sectional bibliometric analysis examined the development of research on deep learning in the field of ophthalmology and its sub-topics from 2015 to 2024.
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