Infodemic describes a special state during a public health emergency in which biased health information shows an explosive spread. This study systematically comprehends the definition, spreading mechanism, and coping strategies of infodemics. The study shows that the proliferation of false information relies on social media platforms and is driven by group psychological factors, social networks and algorithmic recommendation mechanisms, presenting complex dynamic characteristics. This article focuses on analyzing the commonly used tools in the field of communication science (including three types of models, ABM, SIR and MAP) and exploring their application value in simulating the diffusion path of disinformation. In terms of management strategies, the article proposes the strategies of constructing an intelligent information monitoring system, enhancing public health literacy, strengthening information filtering and quality control, and realizing the precise transformation of scientific knowledge. Despite the progress of the research, it still faces challenges such as fast dissemination speed and difficult supervision. In the future, interdisciplinary cooperation should be strengthened, a data-based information intervention system should be constructed, and the public's ability to immunize false information should be enhanced to protect public health safety.
It has been 36 years since the first version of essential medicine list (EML) was released by WHO in 1977,when 18 versions of WHO-EML and four versions of children essential medicine list have been released. In 1982, the first version of national essential medicine list (NEML) was released in China. Till 2012, there were eight versions of NEML in total. This paper introduces WHO-EML in aspects of origin, idea, definition, design, and innovation of selection methodology,principle, and workflow; compares the evolution, design, selection methodology between WHO-EML and Chinese NEML; and points out the challenges of evaluation and decision making of Chinese NEML.
The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) has been widely accepted in the assessment of diagnostic accuracy and quality. However, it is not suitable for assessing risk of bias in studies comparing diagnostic accuracy. The current common practice in systematic reviews is to derive comparative accuracy from non-comparative diagnostic accuracy studies, which is inherently biased. The QUADAS group developed the QUADAS-Compare (QUADAS-C) tool for assessing the risk of bias in comparative diagnostic accuracy studies. It was officially launched in October 2021. QUADAS-C retains the same 4-domain structure as QUADAS-2: patient selection, index test, reference standard, and flow and timing. It also includes an additional 14 signaling questions and 4 risk of bias questions. This allows researchers to identify high-quality research evidence and avoid bias in research design and conduct. This article interpreted the basic situation, evaluation items, evaluation standards, and usage methods and procedures associated with QUADAS-C to provide references for domestic users.
Hospital-based health technology assessment (HB-HTA) is an indispensable method and measure to improve the level of fine management and establish a value-based medical service system. This paper introduces the differences between HB-HTA and HTA, four organizational management models, assessment process, assessment steps and HB-HTA reporting standardization, which provides a reference for evaluating organizations.
目的 通過關鍵詞共現分析和作者共現分析的方式，研究國內外循證醫學教育領域的研究熱點、學科分支和發展趨勢。 方法 通過檢索中國學術期刊數據庫（CNKI）和Medline數據庫，分別收集相關研究文獻，并提取關鍵詞詞頻、作者發文數、核心期刊和核心研究單位，對核心關鍵詞和核心發文作者建立共現矩陣，使用Ucinet繪制可視化網絡圖。 結果 成功得到核心關鍵詞和核心作者可視化網絡。中文可視化網絡包括“循證醫學”、“護理”、“教育”、“教學”4個方面，英文可視化網絡包括“evidence-based medicine”、“nursing”、“education”、“organization amp; administration”4個方面，國內外該學科發展趨勢相似。國內循證醫學教育以四川大學為核心，合作網絡較大。同時國內核心期刊與國外側重點不同。 結論 國內循證醫學教育研究領域總體發展趨勢良好，在醫學教育中起著極為重要的作用。Objective To built visualized networks of evidence-based medical education in and out of China by using co-occurrence analysis of key words and authors. Methods We searched the China National Knowledge Infrastructure (CNKI) database in Chinese and Medline database in English, collecting the related articles, and took out the core key words, core authors and core administrations. Then we built the matrix of key words and authors, and finally finished visualized network by Ucinet. Results We successfully obtained the visualization of this subject. Chinese network contained four parts: “evidence-based medicine”, “nursing”, “education” and “teaching”; while English network also contained four parts: “evidence-based medicine”, “nursing”, “education”, “organization” and “administration”. In China, Sichuan University located in the center of the network of authors, and also leading in the administrations. The network of authors in China showed a larger cooperation than overseas. And the core-periodicals had different emphases. Conclusion The evidence-based medical education develops well in China, and plays an important role in the medical education.
ObjectiveTo analyze publications of the application of artificial intelligence related methods in medicine.MethodsPubMed and EMbase databases were electronically searched. Pathfinder Networks (PFNETs) algorithm, co-word network analysis and visualization technology were applied to analyze the time trend, journal distribution, and co-word structure of high-frequency medical keywords in key journals.ResultsThe amount of literature published on the application of artificial intelligence related methods in the medical field had been increasing annually. Nowadays, the number of studies published in the United States was the largest, and that in China, it was the sixth (first in developing countries). The number of the first author from the United States or China were among the top two, which were significantly more than any other regions. In 2012, IEEE Trans Neural Netw Learn Syst in the computer field became one of the major contributing journals. In recent years, the methods and applications proposed in the medical field were closely related to natural language processing, neural networks, and support vector machines.ConclusionsAt present, the United States is in a leading position in terms of artificial intelligence in medicine, and China has also abundant research strength. The number of medical literature published in interdisciplinary journals is increasing gradually, showing that the research and application of artificial intelligence related methods in medicine have become a research hotspot in recent years.
【摘要】 目的 通過關鍵詞共現分析和作者共現分析的方式，研究國內外循證心血管領域的研究熱點、學科分支和發展趨勢。 方法 通過檢索中國生物醫學數據庫和Medline數據庫，分別收集相關研究文獻，并提取關鍵詞詞頻、作者發文數和核心研究單位，對核心關鍵詞和核心發文作者建立共現矩陣，使用Ucinet繪制可視化網絡圖。 結果 成功得到核心關鍵詞和核心作者可視化網絡。中文可視化網絡包括循證醫學、治療和護理3個方面，英文可視化網絡包括evidence-based medicine、therapy和treatment outcome 3個方面。而在作者合作可視化網絡中，國內作者合作關系較為松散，合作團體較小，國外作者合作關系較為緊密，合作團體較大。國內核心研究機構分布于循證研究重點高校，國外則以發達國家為主。 結論 國內循證心血管研究領域總體發展趨勢較好，但較國外相比仍存在一些不足，需要進一步加強重點研究，調整研究規劃。【Abstract】 Objective To research on the hot issues, branch system and development trend of evidence-based cardiovascular medicine in and out of china by using co-word analysis of keywords and authors. Methods By Searching the CBM database in Chinese and Medline database in English, we collected the related articles and picked out the frequency of keywords, authors and core administrations, and built the matrix of keywords and authors. Finally we completed the visualized network by Ucinet. Results We successfully obtained the visualization of this subject. Chinese network contained three parts: evidence-based medicine, therapy and nursing, while English network also contained three parts: evidence-based medicine, therapy and treatment outcome. In the authors′ visualized network, Chinese authors had less cooperation and looser relationship compared with their counterparts overseas. The core administrations in china located in outstanding universities, and developed countries had the dominated roles in the world. Conclusions The development trend of evidence-based cardiovascular medicine in china is good, but still has some limitations compared with overseas studies. More emphasis should be put on strengthening core-area researching and research plan adjustment.
The QUADAS-2, QUIPS, and PROBAST tools are not specific for prognostic accuracy studies and the use of these tools to assess the risk of bias in prognostic accuracy studies is prone to bias. Therefore, QUAPAS, a risk of bias assessment tool for prognostic accuracy studies, has recently been developed. The tool combines QUADAS-2, QUIPS, and PROBAST, and consists of 5 domains, 18 signaling questions, 5 risk of bias questions, and 4 applicability questions. This paper will introduce the content and usage of QUAPAS to provide inspiration and references for domestic researchers.
Currently, the medical imaging methods based on artificial intelligence are developing rapidly, and the related literature reports are increasing year by year. However, there is no special reporting standard, and the reporting of the results is not standardized. In order to improve the report quality of this kind of research and help readers and evaluators evaluate the quality of this kind of research more scientifically, a checklist for artificial intelligence in medical imaging (CLAIM) was put forward abroad. This paper introduces the content of CLAIM and explains its items.
Objective To understand the situation of commonly-used drugs, medical device and their storages in rural households among model well-off township hospitals in eastern, central and western China, and to provide the basis for the guidance of reasonably using and scientifically storing drugs. Methods The methods of combining simple random sampling and cluster sampling were used to investigate and analyze the situation of commonly-used drugs, medical device and their storages in 162 households from three well-off township hospitals in Shanghai, Zhejiang, and Sichuan provinces, respectively. Results The storage rates of commonly-used drugs of rural households in well-off towns were cold medicine (72.2%), wound paste (51.9%), cooling oil (39.5%), essential balm (36.4%), antihypertensive (27.8%), iodine tincture (14.2%), anti-diabetic drugs (13.0%) and other drugs (17.3%). The storage rates of medical devices were thermometer (50.0%), cotton swab (47.5%), sphygmomanometer (9.3%), injector (1.2%) and other devices (22.2%). A total of 66% of respondent families stored drugs and medical devices in a fixed drawer. Only 3.1% families stored drugs and medical devices in the special portable medical kit. Conclusion Rural families have a higher rate of household drugs among model well-off township hospitals in eastern, central and western China, and most drugs are OTC drugs. The storage rates of medical devices are not high. Many rural family-owned medical devices are linked with special chronic diseases in the family. A lot of rural families place drugs and medical devices randomly. There are many security risks, and it may affect the rational utilization of drugs.