Objective To compare and review worldwide journals titled “evidence-based” in order to provide an overview of these healthcare journals and suggestions for improving the quality of this type of journal in China and to introduce a quick way for healthcare professionals and patients to obtain high quality clinical evidence. Methods We searched PubMed, EMBASE, Ulrich’s Periodicals Directory, Wanfang and some relevant websites to identify journals titled “evidence-based”. The last issues in 2006 of these kind of journals were analyzed by bibliometric method. Results Twenty-four journals were included. Covering 12 disciplines such as clinical, nursing, health care, etc. They were divided into the primary journals and the secondary journals, published in two languages in six countries. These journals had differences and similarities in their aims, columns and contents, etc. Conclusions Evidence-based medical journals provide the latest developments and the best evidence for healthcare professionals in their own fields, save their time in searching for and reading information, and provide ideas for improving the quality of similar journals in our country.
Objective To conduct a systematic and comprehensive bibliometric analysis related to medical imaging in coronavirus disease 2019 (COVID-19) published by Chinese authors, and indicate the current status and guidance for coping with the challenges brought by long COVID. Methods Literature records pertaining to COVID-19 imaging were retrieved from the Web of Science (WOS) and Chinese Science Citation Database (CSCD) with a date range of January 1st, 2020 to December 31st, 2022. CiteSpace was used to analyze the popular topics and generate a visual map to further refine research trends. Results The search enrolled 2229 publications (1771 publications from WOS and 458 from CSCD). The monthly volume of publications showed a significant increase followed by a gradual decline, and then maintained a stable level. Clustering analysis of keywords and co-citations revealed that the research hotspots in COVID-19 imaging were clinical features, imaging differential diagnosis, and the application of artificial intelligence. Keyword burst analysis showed that “deep learning” was the most intensive keyword, while “attention mechanism” had the longest burst duration. Conclusions In recent years, the research in the field of COVID-19 imaging has made steady progress. Artificial intelligence has received the most attention. Clinical features, imaging features and artificial intelligence assisted diagnosis are the top of most concerns. The future focus of COVID-19 imaging research may be on improving algorithm models to expand the application of artificial intelligence in disease diagnosis, treatment, and management.
ObjectiveTo investigate the hotspots from researches on imaging of pancreatic neuroendocrine tumor in recent five years. MethodsThe bibliographies from research literatures on imaging of pancreatic neuroendocrine tumor from 2010 to 2015 in PubMed database were downloaded. The Bicomb 2.0 bibliographies analysis software was used to count high-frequency of Mesh major topics (MJMEs). SPSS 22.0 statistical software was applied for clustering analysis with MJMEs, then to get the topic hotspots. ResultsA total of 357 literatures were screened out during the years of 2010-2015. The MJMEs which frequency > 13 were 28. Taken the 28 MJMEs into clustering analysis, then three research hotspots were clustered. ConclusionResearches on imaging of the pancreatic neuroendocrine tumor in recent five years are mainly in terms of imaging techniques, a comparative study of pathology and endoscopic ultrasonography-fine needle aspiration, imaging and disease treatment.
With the development of artificial intelligence (AI) technology, great progress has been made in the application of AI in the medical field. While foreign journals have published a large number of papers on the application of AI in epilepsy, there is a dearth of studies within domestic journals. In order to understand the global research progress and development trend of AI applications in epilepsy, a total of 895 papers on AI applications in epilepsy included in the Web of Science Core Collection and published before December 31, 2022 were selected as the research objects. The annual number of papers and their cited times, the most published authors, institutions and countries, and their cooperative relationships were analyzed, and the research hotspots and future trends in this field were explored by using bibliometrics and other methods. The results showed that before 2016, the annual number of papers on the application of AI in epilepsy increased slowly, and after 2017, the number of publications increased rapidly. The United States had the largest number of papers (n=273), followed by China (n=195). The institution with the largest number of papers was the University of London (n=36), and Capital Medical University in China had 23 papers. The author with the most published papers was Gregory Worrell (n=14), and the scholar with the most published articles in China was Guo Jiayan from Xiamen University (n=7). The application of machine learning in the diagnosis and treatment of epilepsy is an early research focus in this field, while the seizure prediction model based on EEG feature extraction, deep learning especially convolutional neural network application in epilepsy diagnosis, and cloud computing application in epilepsy healthcare, are the current research priorities in this field. AI-based EEG feature extraction, the application of deep learning in the diagnosis and treatment of epilepsy, and the Internet of things to solve epilepsy health-related problems are the research aims of this field in the future.
Objective To investigate the current status of systematic reviews/meta-analyses published by pharmacists in hospital in China by using bibliometric analysis and assessing the methodological quality of these studies. Methods The literatures were searched from CNKI, WanFang Data, VIP, CBM, CMCI, PubMed, EMbase, The Cochrane Library (Issue 2, 2016) from the establishment to March 17th, 2016. According to the inclusive and exclusive criteria, the authors independently screened literature and extracted the data. Methodology quality and reporting quality were evaluated by using AMSTAR tool and PRIMSA statement. Data analysis was conducted by using Excel 2013 and SPSS 20.0 software. Results A total of 1 018 systematic reviews/meta-analyses were included of which 871 were published in Chinese and 147 were in English. The published literature increased year by year, as well as the reporting quality and methodological quality. All of the included studies were published in 146 Chinese journals and 97 English journals. The authors were from 308 hospitals of 27 provinces. Drug effectiveness and drug safety were mainly assessed, and western medicine was the main category. Most of these studies were focused on anti-tummor drugs. Conclusion Evidenced-based drug evaluation by hospital pharmacists in China is rapidly increasing as well as the methodological quality and reporting quality, however, the development is unbalanced in China, and evidence-based medicine should be further promoted in the field of hospital pharmacy.
This is the fourth paper in the evidence-based medicine glossary series. Bibliometric method was used to analyse the development of evidence-based medicine in medicine and non-medicine system. Evidence-based medicine, Evidence-based nursing, Evidence-based practice, Evidence-based dentistry, Evidence-based emergency medicine have been defined in this paper.