ABSTRACT
With the continuous progress of research methodology in the real world and the growing maturity of artificial intelligence technology， a method for conducting “quantitative” research to guide clinical practice based on traditional Chinese medicine （TCM） diagnosis and treatment data was gradually developed. However， there is still a need for further improvements in the overall design of studies and the transformation of findings into clinical practice. Based on this， we put forward a comprehensive overall design concept and application approach for real-world study and artificial intelligence research based on clinical diagnosis and treatment data of TCM. This approach consists of five steps： Constructing a research-based database with a large sample size and high data quality； Mining and classification of core prescriptions； Conducting cohort studies to evaluate the effectiveness of core prescriptions； Utilizing case-control studies to clarify the dominant population； Establishing predictive models to achieve precision medicine. Additionally， it is imperative for researchers to establish a standardized system for collecting TCM variables and processing data， optimize the determination and measurement methods of confounding factors， further improve and promote methodologies， and strengthen the training of interdisciplinary talents. By following this research method， we anticipate that the clinical translation of research findings will be facilitated， leading to advancements in TCM precision medicine. Real-world study and artificial intelligence research share similar research foundations， and clinical applications complement each other. In the future， the two will merge together.
ABSTRACT
This study firstly reviews the history and current situation of course construction, then combines closely with the objectives and the summary of current situation of the standardized residency training, and finally puts forward concrete countermeasures from the several angles, such as teaching objectives, teaching team development, teaching content and forms, as well as evaluation mechanism, advocating the promotion of problem-based learning (PBL), medical simulation teaching (MST), literature discussion and other teaching methods, encouraging the integration of humanities education and clinical guidelines into teaching, and adopting objective structured clinical examination + standardized patient (OSCE+SP) assessment method and conducting two-way assessment, so as to assist in the education development of the standardized residency training.
ABSTRACT
Gastrointestinal stromal tumor (GIST) is a relatively rare type of gastrointestinal tract tumors. Thus, it is difficult to perform randomized controlled trials (RCTs) of GIST in a single center, which are often plagued by a small number of participants. Real world study (RWS) is a complement to the evidence derived from traditional RCT. Emerging sources of real world data offer enormous opportunity for deeper understanding of why treatments work (or not) and for whom. Evidence generated from RWD can help clarify best use of treatments for individuals and populations, and care value. Thus, RWS of GIST has attracted much attention. RWS helps us better understand the diagnosis, treatment, prognosis and outcome prediction of GIST in clinical practice. GIST is often misdiagnosed as other tumor. A diagnostic test provides evidence on how well a test correctly identifies or rules out GIST. Therapeutic research aims to evaluate the effectiveness and/or safety of a therapy for GIST. Prognostic research aims to forecast the likely outcome of GIST, explore the factors affecting the outcome, and analyze quality of life. Predictive research aims to quantify the probability of identification or health outcome of GIST based on a set of predictors. Pharmacoeconomic data in real world can evaluate the cost-effectiveness of medicinal products for GISTs and serve as a guidance tool for optimal healthcare resource allocation. Attaching importance to data sourse and data quality, strenthening communicate with a qualified statistician, and the implementation of a standardized process are necessary for performing a high-quality RWS.