Randomized controlled trials (RCT) 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 RCT. 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 RCT 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.
The method of network meta-analysis of diagnostic test accuracy is in the exploratory stage. We had explored and introduced several methods of network meta-analysis of diagnostic test accuracy before. Based on example, we introduce ANOVA model for performing network meta-analysis of diagnostic test accuracy step-by-step.
It is a challenge for clinicians and diagnostic systematic reviewers to determine the best test in clinical diagnosis and screening. Meanwhile, it also becomes the new chance and challenge for diagnostic test meta-analysis. Network meta-analysis has been commonly used in intervention systematic reviews, which can compare the effect size of all available interventions and to choose the best intervention. Network meta-analysis of diagnostic test can be defined as comparing all available diagnostic technologies in the same conditions based on the common reference tests. In order to provide the guide for diagnostic systematic reviewers, we aims to introduce four methods of conducting diagnostic test accuracy network meta-analysis, and to explore two ranking methods of network meta-analysis of diagnostic test accuracy.
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.