Background With the widespread application of a robot to surgery, growing literature related to robotics in surgery (RS) documents widespread concerns from scientific researchers worldwide. Although such application is helpful to considerably improve the accuracy of surgery, we still lack the understanding of the multidiscipline-crossing status and topic distribution related to RS. Objective The aim of this study was to detect the interdisciplinary nature and topic hotspots on RS by analyzing the current publication outputs related to RS. Methods The authors collected publications related to RS in the last 21 years, indexed by the Web of Science Core Collection. Various bibliometric methods and tools were used, including literature distribution analysis at the country and institution level and interdisciplinary collaboration analysis in the different periods of time. Co-word analysis was performed based on the keywords with high frequency. The temporal visualization bar presented the evolution of topics over time. Results A total of 7732 bibliographic records related to RS were identified. The United States plays a leading role in the publication output related to RS, followed by Italy and Germany. It should be noted that the Yonsei University in South Korea published the highest number of RS-related publications. Furthermore, the interdisciplinary collaboration is uneven; the number of disciplines involved in each paper dropped from the initial 1.60 to the current 1.31. Surgery; Engineering; Radiology, Nuclear Medicine, and Medical Imaging; and Neurosciences and Neurology are the 4 core disciplines in the field of RS, all of which have extensive cooperation with other disciplines. The distribution of topic hotspots is in imbalanced status, which can be categorized into 7 clusters. Moreover, 3 areas about the evolution of topic were identified, namely (1) the exploration of techniques that make RS implemented, (2) rapid development of robotic systems and related applications in surgery, and (3) application of a robot to excision of tissues or organs targeted at various specific diseases. Conclusions This study provided important insights into the interdisciplinary nature related to RS, which indicates that the researchers with different disciplinary backgrounds should strengthen cooperation to publish a high-quality output. The research topic hotspots related to RS are relatively scattered, which has begun to turn to the application of RS targeted at specific diseases. Our study is helpful to provide a potential guide to the direction of the field of RS for future research in the field of RS.

BackgroundWidespread adoption and continued developments in mobile health care technologies have led to the improved accessibility and quality of medical services. In China, WeChat, an instant messaging and social networking app released by the company Tencent, has developed a specific type of user account called WeChat official account (WOA), which is now widely adopted by hospitals in China. It enables health care providers to connect with local citizens, allowing them to, among other actions, send regular updates through mass circulation. However, with the diversity in function provided by WOA, little is known about its major constitution as well as the influence factors on the WeChat communication index (WCI). The WCI has been widely used in social media impact ranking with various types of WeChat content to fully reflect the dissemination and coverage of tweets as well as the maturity and impact of WOA.ObjectiveThere are two typical WOAs available to users, namely, WeChat subscription account (WSSA) and WeChat service account (WSVA). The biggest difference between them is the frequency of messages transmitted. This study aimed to explore the function constitution of WSVA adopted by top tertiary hospitals in China and the major contributors of the WCI score.MethodsA total of 681 top tertiary hospitals were selected from the Hospital Quality Monitoring System; the WOA of every top tertiary hospital was retrieved in the WeChat app. We divided core functional items of WSVAs using categorical principal component analysis. To elicit the factors that influenced the use of WSVA, quantile regression was employed to analyze the WCI score.ResultsFrom the 668 WOAs identified, adoption of WSVAs (543/668, 81.3%) was more than that of WSSAs (125/668, 18.7%). Functional items of WSVAs were categorized into four clusters: (1) hospital introduction, (2) medical services, (3) visiting assistants, and (4) others. With regard to the influence factors on the WCI, the impact of the activity index of WSVA and the total visiting number of outpatients and emergencies on WCI were statistically significant and positive in all quantiles. However, the year of certification, the type of hospital, the year of public hospital reform, and the number of beds merely affected the WCI at some quantiles.ConclusionsOur findings are considered helpful to tertiary hospitals in developing in-depth functional items that improve patient experience. The tertiary hospitals should take full advantage of times of posting and provide high-quality tweets to meet the various needs of patients.

Background There are more than 6000 rare diseases in existence today, with the number of patients with these conditions rapidly increasing. Most research to date has focused on the diagnosis, treatment, and development of orphan drugs, while few studies have examined the topics and emotions expressed by patients living with rare diseases on social media platforms, especially in online health communities (OHCs). Objective This study aimed to determine the topic categorizations and sentiment polarity for albinism in a Chinese OHC, Baidu Tieba, using multiple methods. The OHC was deeply mined using topic mining, social network analysis, and sentiment polarity analysis. Through these methods, we determined the current situation of community construction, identifying the ongoing needs and problems experienced by people with albinism in their daily lives. Methods We used the albinism community on the Baidu Tieba platform as the data source in this study. Term frequency–inverse document frequency, latent dirichlet allocation models, and naive Bayes were employed to mine the various topic categories. Social network analysis, which was completed using the Gephi tool, was employed to analyze the evolution of the albinism community. Sentiment polarity analysis was performed using a long short-term memory algorithm. Results We identified 8 main topics discussed in the community: daily sharing, family, interpersonal communication, social life and security, medical care, occupation and education, beauty, and self-care. Among these topics, daily sharing represented the largest proportion of the discussions. From 2012 to 2019, the average degree and clustering coefficient of the albinism community continued to decline, while the network center transferred from core communities to core users. A total of 68.43% of the corpus was emotional, with 35.88% being positive and 32.55% negative. There were statistically significant differences in the distribution of sentiment polarity between topics (P<.001). Negative emotions were twice as high as positive emotions in the social life and security topic. Conclusions The study reveals insights into the emotions expressed by people with albinism in the Chinese OHC, Baidu Tieba, providing health care practitioners with greater appreciation of the current emotional support needed by patients and the patient experience. Current OHCs do not exert enough influence due to limited effective organization and development. Health care sectors should take greater advantage of OHCs to support vulnerable patients with rare diseases to meet their evidence-based needs.

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Based on the dynamic characteristics of electric vehicles, this study describes the use of existing basic parameters of a specific electric vehicle to optimize the performance parameters of an asynchronous motor. In addition, a theoretical reference of such an asynchronous motor is provided.