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.
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.
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.