This paper explores the methodological characteristics and key considerations of umbrella trials. By allocating different treatment strategies based on patients' molecular features, umbrella trials significantly enhance screening efficiency and can quickly identify ineffective therapies. Through the analysis of patient allocation strategies, statistical model selection, and error control methods, we can better utilize this design to accelerate drug development and achieve more efficient personalized treatment. However, despite significant progress in methodology and practice, umbrella trials still face multiple challenges during implementation, including trial design, sample size calculation, patient recruitment, informed consent, and resource allocation. Addressing these challenges in the future will help further optimize the application of umbrella trials. This study aims to provide thoughts and inspirations for researchers conducting umbrella trials and promote the steady development of this field.
Clinical practice guidelines (CPGs) serve as the cornerstone of medical decision-making, with evaluation tools such as AGREE and RIGHT designed to ensure that these guidelines are grounded in the best available evidence and contribute to enhancing healthcare quality. This article reviews the historical development and current status of CPG evaluation tools, examining their diversity, complexity, application challenges, and inconsistencies in evaluation outcomes. A thorough discussion is provided on the strengths and weaknesses of existing evaluation tools, along with proposed future developmental directions. It is recommended that future efforts prioritize the creation of more streamlined tool designs, foster enhanced international collaboration strategies, and incorporate artificial intelligence technologies. These initiatives aim to improve both the efficiency and accuracy of evaluative processes while facilitating advancements in healthcare practices towards elevated quality standards.
Randomized controlled trials (RCTs) are the gold standard for evaluating the effectiveness of medical interventions, and the quality of their reporting is critical to clinical practice and scientific research. Consolidated standards of reporting trials (CONSORT) statements and their extended tools are the core standards for reporting quality of RCTs. Since its first publication in 1996, it has been updated and expanded several times, and has become the authoritative guideline in the field of clinical research worldwide. This paper systematically reviews the evolution of CONSORT from its foundation version in 1996 to the latest version in 2025, and analyzes the classification and application scenarios of five categories of CONSORT extension tools (research design/analysis type oriented, subject domain oriented, reporting process-oriented, research focus oriented, and interdisciplinary integration oriented) in detail. CONSORT has significantly improved the transparency, reproducibility, and scientific rigor of RCTs reporting, especially in reducing the risk of bias, standardizing data sharing, and promoting open science. However, its implementation still faces challenges such as concept misuse, insufficient cross-cultural adaptation, resource limitation, and mechanization. The CONSORT system needs to be promoted into high-quality practice worldwide through the development of multilingual resources, the application of intelligent tools and international cooperation, so as to provide continuous support for the standardization and evidence transformation of clinical research.
ObjectiveTo analyze the 2023 learning society construction project in order to provide references for researchers in this field. MethodsExcel 2021 software was used to summarize and comb the list of key tasks for the construction of a learning society in 2023 (field of higher continuing education) published on the official website of the Chinese Ministry of Education, and to visually analyze the research topics of key tasks in the medical field and the distribution of applicants. ResultsThe analysis found that a total of 250 projects were shortlisted in the cultivation and construction list, including 100 teaching reform and innovation tasks of continuing education for academic degrees, 100 reform and innovation tasks of non-academic education, and 50 tasks to explore the path of coordinated innovation of the three education. The project involved digital transformation, education and teaching reform, ideological and political education, etc. There were 17 medical projects, accounting for 6.8% of the total number of key tasks. The 17 medical key task declaration units were distributed in 12 provinces (regions), which were mainly concentrated in East China, and the construction of "non-double first-class" universities as the main force; The results mainly focused on personnel training and education and teaching reform. ConclusionThe analysis results of the key task list of 2023 learning society construction (field of higher continuing education) provide important references and enlightenment for the researchers in the field of education, and provide guidance and references for the future development of higher continuing education.
ObjectiveTo systematically map the development landscape of micro-majors in China, analyze their current research status and core issues, and explore future strategies for their high-quality development. MethodsThis study adopts a research paradigm combining policy text analysis with bibliometric methods. It systematically reviews national and local policy documents concerning micro-majors, higher education reform, and industry-education integration to clarify the logic of policy drivers. Concurrently, it retrieves core journal literature on micro-majors and employs tools such as VOSviewer, R software, and Origin to conduct visual analyses across dimensions including publication trends, high-frequency keyword clustering, institutional distribution, and disciplinary intersections. ResultsChina's micro-majors have evolved through phases of independent exploration, rapid expansion, and standardized national development. The "Double Thousand" initiative has integrated them into the country's top-level educational framework. Research focuses on four key areas: talent cultivation, industry-education integration, interdisciplinary collaboration, and pedagogical reform. In practice, science and engineering universities lead the development, though regional distribution remains uneven. The disciplinary landscape has formed a multi-hub, cross-disciplinary empowerment model. ConclusionMicro-majors have evolved from supplementary learning tools into a vital component of the nation's strategic talent reserve.
The contradiction between the exponential growth of medical knowledge and the delay in traditional evidence synthesis has necessitated the living evidence (LE) synthesis model, which focuses on continuous updates. The concept of LE synthesis has evolved into a dynamic full-lifecycle spectrum ranging from evidence maps and systematic reviews to clinical guidelines. Although the framework by Bendersky et al. established a methodological baseline for regulating this complex system, its reliance on human labor and a "process-driven" logic creates significant efficiency bottlenecks when dealing with massive heterogeneous data. In the context of the technological revolution driven by AI and large language models (LLMs), this paper analyzes the limitations of current frameworks and demonstrates the necessity of evolving from "mechanical process execution" to a paradigm of "human-machine collaborative cognitive augmentation". This shift demands a restructuring of how evidence is discovered and integrated. Furthermore, by combining China's advantages in clinical data resources with current standardization efforts, we explore localized pathways that integrate technological foresight with evidence-based rigor. This aims to build a smart, dynamic evidence ecosystem dedicated to high-quality health decision-making.
The burgeoning application of large language models (LLM) in healthcare demonstrates immense potential, yet simultaneously poses new challenges to the standardization of research reporting. To enhance the transparency and reliability of medical LLM research, an international expert group published the TRIPOD-LLM reporting guideline in Nature Medicine in January 2024. As an extension of the TRIPOD+AI guideline, TRIPOD-LLM provides detailed reporting items specifically tailored to the unique characteristics of LLMs, including general foundational models (e.g., GPT-4) and domain-specific fine-tuned models (e.g., Med-PaLM 2). It addresses critical aspects such as prompt engineering, inference parameters, generative evaluation, and fairness considerations. Notably, the guideline introduces an innovative modular design and a "living guideline" mechanism. This paper provides a systematic, item-by-item interpretation and example-based analysis of the TRIPOD-LLM guideline. It is intended to serve as a clear and practical handbook for researchers in this field, as well as for journal reviewers and editors responsible for assessing the quality of such studies, thereby fostering the high-quality development of medical LLM research in China.
Systematic reviews and meta-analyses have become the cornerstone methodologies for integrating multi-source research data and enhancing the quality of evidence. Traditional meta-analyses often demonstrate limitations when handling multiple treatment options. Network meta-analysis (NMA) overcomes these limitations by constructing a network of evidence that encompasses various treatment options, allowing for the simultaneous comparison of both direct and indirect evidence across multiple treatment plans. This provides more comprehensive and precise support for clinical decision-making. This article comprehensively reviews the statistical principles of NMA, its three fundamental assumptions, and the statistical inference framework. It also critically analyzes the mainstream NMA software and packages currently available, such as R (including gemtc, netmeta, rjags, pcnetmeta), Stata (mvmeta, network), WinBUGS, SAS, ADDIS, and various online applications, highlighting their strengths, weaknesses, and suitable scenarios. This analysis provides researchers with a scientific and unified framework for conducting clinical studies and policy-making.