Objective To analyze the citation classics articles， and approach the research development history and the research direction in the future about surgical treatment for type 2 diabetes. Methods The most frequently cited articles had published in Social Sciences Citation Index database by the end of October 30， 2012 were retrieved. The 50 most frequently cited articles were selected. Articles were evaluated for several characteristics， including number of citations， publication time， country of origin， institution， journal， publication type of article， and authorship. Results The most frequently cited article received 1 751 citations and the least frequently cited article received 73 citations， with a mean of 242.76 citations per article. These citation classics were published in 18 high-impact journals， led by Annals of Surgery and Obesity Research as 10 papers. Of the 50 articles， 18 articles were clinical observational study， 20 articles concerned basic science， 10 articles were review articles， and 2 articles were commentary. These citation classics were published from 1990 to 2009， most of them （40） from 2000 to 2009. Three institutions produced 2 top-cited articles， including Medical College of Virginia， Monash University， and East Carolina University. These articles originated from 14 countries， the top was USA （22 articles）. Two persons authored 3 published papers （Cummings DE and Rubino F）. Conclusion Most “citation classics” in research about surgery for type 2 diabetes are observational studies published in high-impact journals by US-based authors after 1990.
ObjectiveTo investigate the citation status of systematic reviews on imaging diagnosis in clinical practice guidelines (CPGs) and provide reference for the development of Chinese imaging diagnosis guidelines. MethodsWe electronically searched PubMed databases to collect systematic reviews on imaging diagnosis. The date was limited from January 1st 2010 to December 31th 2012. Two reviewers independently screened literature and extracted data. The citation data of included systematic reviews were obtained on the Web of Science. Citation analysis method was used to analyze the citation frequency of systematic reviews on imaging diagnosis in CPGs. Results292 systematic reviews on imaging diagnosis were included, of which 94% (275/292) were indexed by Science Citation Index. The total citation frequency of these systematic reviews was 5413 (medium:20, range:0 to 131). 28% (78/275) were cited by CPGs. Of which, 7% (19/275) were used as the source of the evidence of recommendations in CPGs. ConclusionThe ratio of systematic reviews cited by CPGs is low, the ratio of being the source of evidence of recommendations of systematic reviews in CPGs is lower, and furthermore, the citation is time-delayed.
ObjectivesTo investigate the citation status of systematic reviews (SRs)/meta-analyses in clinical practice guidelines and consensuses of traditional Chinese medicine (TCM).MethodsWe electronically searched PubMed, CBM, WanFang Data and CNKI databases to collect TCM guidelines and consensus from January 1st, 2009 to December 31st, 2018. Two reviewers independently screened literature and extracted data. Citation analysis method was used to analyze the citation status of SRs/meta-analysis in TCM guidelines and consensuses.ResultsA total of 142 TCM guidelines and consensuses were included, of which 39 (26.5%) failed to provide relevant citations. Of the 103 (72.5%) TCM guidelines and consensuses providing citations, 48 (34.3%) cited SRs/meta-analyses, and 43 cited outdated SRs/ meta-analyses. Four TCM guidelines and consensuses cited Cochrane reviews. In terms of citations, the average citations of guidelines and consensuses were 35.1 and 42.2, respectively; and the average SRs/meta-analyses citations of guidelines and consensuses were 3.8 and 5.5, respectively.ConclusionsTCM guidelines and consensuses citation report rates and the proportion of citation SRs/meta-analyses still require increase. TCM guidelines developers should strengthen the role and significance of SRs, especially Cochrane reviews, in supporting recommendations.
In recent years, the research on artificial intelligence medical devices has risen markedly along with the expanding application scenarios, exhibiting prominent interdisciplinary characteristics. From 2000 to 2024, the variety of research in artificial intelligence medical devices has significantly increased, while the balance of disciplines has slightly declined, and Simpson's diversity index has continuously increased. Medicine and biology are the main research themes and supportive disciplines in this field. Knowledge from computer science, engineering technology, and mathematics is widely involved and shows an upward trend, while content from the humanities and social sciences is less involved in the research. Compared to the United States and the United Kingdom, China has relatively less biological and chemical knowledge content in the research of this field, but more content related to computer science, engineering technology and material science is involved. This study analyzes the current state and trends of interdisciplinary on artificial intelligence medical devices from the perspective of macro-categories of disciplines, aiming to provide references for research planning, talent training and interdisciplinary cooperation in the field.