Clinical prediction models typically utilize a combination of multiple variables to predict individual health outcomes. However, multiple prediction models for the same outcome often exist, making it challenging to determine the suitable model for guiding clinical practice. In recent years, an increasing number of studies have evaluated and summarized prediction models using the systematic review/meta-analysis method. However, they often report poorly on critical information. To enhance the reporting quality of systematic reviews/meta-analyses of prediction models, foreign scholars published the TRIPOD-SRMA reporting guideline in BMJ in March 2023. As the number of such systematic reviews/meta-analyses is increasing rapidly domestically, this paper interprets the reporting guideline with a published example. This study aims to assist domestic scholars in better understanding and applying this reporting guideline, ultimately improving the overall quality of relevant research.
With the increasing availability of clinical and biomedical big data, machine learning is being widely used in scientific research and academic papers. It integrates various types of information to predict individual health outcomes. However, deficiencies in reporting key information have gradually emerged. These include issues like data bias, model fairness across different groups, and problems with data quality and applicability. Maintaining predictive accuracy and interpretability in real-world clinical settings is also a challenge. This increases the complexity of safely and effectively applying predictive models to clinical practice. To address these problems, TRIPOD+AI (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis+artificial intelligence) introduces a reporting standard for machine learning models. It is based on TRIPOD and aims to improve transparency, reproducibility, and health equity. These improvements enhance the quality of machine learning model applications. Currently, research on prediction models based on machine learning is rapidly increasing. To help domestic readers better understand and apply TRIPOD+AI, we provide examples and interpretations. We hope this will support researchers in improving the quality of their reports.
In recent years, the TRIPOD 2015 statement has shown significant limitations with the gradual application of machine learning methods in the development and evaluation of clinical prediction models. Therefore, TRIPOD 2015 statement has been updated in 2024 as the TRIPOD+AI statement entitled "TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods", aiming to promote the complete, accurate, and transparent reporting of studies that develop a prediction model or evaluate its performance. This article interprets the key contents and items of the TRIPOD+AI in order to provide aids for clinical researchers.
[Abstract]As the volume of medical research using large language models (LLMs) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published “TRIPOD-LLM reporting guideline for studies using large language models”. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLMs. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLMs into healthcare.
In recent years, the potential value of clinical big data have been gradually realized, and disease prediction models have begun to become a hot spot in clinical research. Predictive models of different types of diseases play an increasingly important role in individual risk assessment. However, due to the lack of reporting specifications for studies on disease prediction model, the structure and quality of reports are mostly mixed. In 2015, BMJ published a paper entitled "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement" stated that there should be a uniform study of predictive models for disease diagnosis and prognosis. This article interprets key contents of the statement to promote research and understanding of the report specification.