ObjectiveThis study investigates the adherence to ethical principles in doctoral dissertations focused on human as the research subject, aiming to provide a foundation for enhancing ethical awareness among medical doctoral candidates. MethodsUtilizing the Chinese database of doctoral dissertations, a total of 1 733 relevant papers published in 2021 were collected. The study compared ethical considerations among double first-class universities, other high-ranking institutions, different university types, various disciplines, diverse training orientations, enrollment types, and medical doctoral dissertations from different regions. ResultsIn 2021, among Chinese medical doctoral dissertations involving human as the research subject, 73.34% mentioned ethical considerations, and 86.27% mentioned informed consent. Dissertations reporting ethical approval descriptions, approval numbers, ethical approvals, and informed consent constituted only 2.19%. Notably, 12.52% of medical doctoral dissertations failed to incorporate ethical considerations and informed consent details in their content. ConclusionThe ethical awareness of medical doctoral candidates in China and the reporting of ethical information in their dissertations require urgent enhancement and improvement.
Network meta-analysis aims to integrate direct and indirect evidence, make a comprehensive comparison and in-depth analysis of three or more interventions and treatments, compare and rank the advantages and disadvantages of different treatment measures, so as to provide strong evidence for decision-making. However, there may be some bias in the process of making network meta-analysis, analyzing data and interpreting results. Therefore, accurate assessment and proper handling of the risks of bias in network meta-analysis is conductive to improve the quality of decision-making and promote the achievement of good clinical outcomes. At present, the number of published network meta-analysis has increased significantly globally, but the quality remains to be improved, and there is a lack of assessment tools for risks of bias in network meta-analysis. In 2025, Canadian scholar Carole Lunny and colleagues developed The Risk of Bias in Network Meta-Analysis (RoB NMA) tool for evaluating the risk of bias in network meta-analysis and published it in the BMJ, which is important to reduce the bias in network meta-analysis and promote optimal clinical decision-making. This study will interpret it with examples, aiming to help researchers better understand and apply this evaluation tool.
Living systematic reviews (LSR) represent an evolving methodology for systematic review that is continuously updated to incorporate new evidence in a timely manner, ensuring that healthcare professionals and policymaker shave access to the most last information to make optimal decisions. The global publication of LSR has been a rapid increase. But the quality of reporting remains to be enhanced. In 2024, the PRISMA-LSR working group, in conjunction with the characteristics of LSR to form the reporting standards for living systematic reviews, which plays a significant role in promoting the transparent, complete, and accurate reporting of LSR. It has been published in the BMJ journal. This article interpreted PRISMA-LSR with representative examples, aiming to provide a reference for the standardization of LSR by domestic scholars.
ObjectiveTo analyze the research hotspots and development trends of core outcome set (COS) from 2015 to 2024, providing a reference for future research in this field. MethodsWe retrieved literature on COS research from the Web of Science Core Collection and CNKI spanning January 1, 2015 to December 31, 2024. We extracted and organized data on the number of publications, journals, citation frequency, and keywords using Excel 2021. We performed keyword clustering analysis using VOSviewer 1.6.13 and generated strategic coordinate maps using Bibliometrix 3.13 in R 4.3.1. ResultsWe included a total of 1 288 studies, comprising 1 085 English publications and 203 Chinese publications. From 2015 to 2024, the number of COS publications showed a steady increase. English journals covered a wide range of fields, while Chinese journals were mainly focused on traditional Chinese medicine. High-impact articles primarily focused on COS methodology. Chinese literature mainly concentrated on the application of COS in traditional Chinese medicine, while English literature focused on child health, Delphi surveys, quality of life, and pain. The results of the strategic coordinate map showed that research on acupuncture core outcome indicators, qualitative studies of surgical COS, and Delphi-based COS for quality of life in patients with rheumatoid diseases were relatively weak, with significant room for improvement. ConclusionOver the past decade, COS research has shown a steady growth trend and has gradually become an important tool for improving the standardization and scientific rigor of clinical research. As COS research continues to expand, there is increasing overlap in the scope and findings of different studies. Future research could incorporate umbrella and basket study designs to optimize resource utilization and promote the application of COS in clinical practice.
Mendelian randomization (MR) studies use genetic variants as instrumental variables to explore the effects of exposures on health outcomes. STROBE-MR (strengthening the reporting of observational studies in epidemiology using Mendelian randomization) assists authors in reporting their MR studies clearly and transparently, and helpfully to improve the quality of MR. This paper interpreted the STROBE-MR, aiming to help Chinese scholars better understand, disseminate, and apply it.
Accurately assessing the risk of bias is a critical challenge in network meta-analysis (NMA). By integrating direct and indirect evidence, NMA enables the comparison of multiple interventions, but its outcomes are often influenced by bias risks, particularly the propagation of bias within complex evidence networks. This paper systematically reviews commonly used bias risk assessment tools in NMA, highlighting their applications, limitations, and challenges across interventional trials, observational studies, diagnostic tests, and animal experiments. Addressing the issues of tool misapplication, mixed usage, and the lack of comprehensive tools for overall bias assessment in NMA, we propose strategies such as simplifying tool operation, enhancing usability, and standardizing evaluation processes. Furthermore, advancements in artificial intelligence (AI) and large language models (LLMs) offer promising opportunities to streamline bias risk assessments and reduce human interference. The development of specialized tools and the integration of intelligent technologies will enhance the rigor and reliability of NMA studies, providing robust evidence to support medical research and clinical decision-making.
This study comprehensively reviews the theoretical foundations, historical development, practical applications, and potential challenges of network meta-analysis of diagnostic test accuracy (DTA-NMA). DTA-NMA, as a method for evaluating and comparing the accuracy of different diagnostic tests, demonstrates its unique value in improving diagnostic accuracy and optimizing treatment strategies by integrating direct and indirect evidence, providing crucial support for clinical decision-making. However, despite significant progress in methodology and practice, DTA-NMA still faces multiple challenges in implementation, including enhancing research transparency, integrating diverse evidence, accurately assessing bias risks, presenting and interpreting results, and evaluating evidence quality. In the future, further refinement of reporting standards and evidence grading specific to DTA-NMA research will be crucial for the development of this field, facilitating evidence-based efficient medical decision-making and ultimately improving patient outcomes. This study aims to provide scholars conducting DTA-NMA research with reflection and insights to promote the steady development of this field.
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.
The reporting quality of systematic reviews and meta-analyses is fundamental to the value of evidence in evidence-based medicine. As the internationally endorsed standard, the PRISMA statement and its extensive suite of extensions are crucial for standardizing reporting and enhancing transparency. However, a comprehensive, systematic understanding of its entire framework and profound challenges remains inadequate in the academic community. This review aims to systematically delineate and deeply analyze the complete PRISMA reporting guideline framework, evaluate its application value, uncover its implementation challenges, and forecast its future development directions. This paper traces PRISMA's evolution from its predecessor, QUOROM, to PRISMA 2020, highlighting key shifts in its core principles. It systematically constructs a multi-dimensional framework for the PRISMA family for the first time, categorizing its extensions by foundational versions, study design/analysis types, reporting process stages, disciplinary domains, and specific areas of focus, complemented by a forward-looking analysis of tools currently under development. The review delves into the deep-seated challenges in PRISMA's implementation, including misconceptions, inconsistent application, cross-disciplinary adaptability, and methodological limitations. It proposes that its future lies in balancing standardization with flexibility, enhancing globalized application, and deeply integrating with emerging technologies like artificial intelligence. The PRISMA framework has evolved from a mere reporting checklist into a core methodological architecture that promotes standardization throughout the entire evidence synthesis lifecycle. The continuous optimization and proper application of this framework are of critical theoretical and practical significance for enhancing the overall quality and impact of evidence synthesis research globally.
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.