Structured template and reporting tool for real world evidence (STaRT-RWE) was developed by a team led by professor Shirley V Wang of Brigham and Women's Hospital, Harvard Medical School, which is to plan and report on the implementation of real world evidence (RWE) studies on the safety and efficacy of treatments. The template, published in the journal BMJ in January 2021, has been endorsed by the International Society of PharmacoEpidemiology and the Transparency Initiative promoted by the International Society of Pharmacoeconomics and Outcome Research. This article interprets its entries to promote the understanding and application of STaRT-RWE by domestic scholars engaged in real world study, and help to improve the transparency, repeatability, and accuracy of RWE research.
Chinese medicine case report, a special method, records the experience of practitioners and guides students to inherit and develop Chinese medicine. It can transfer a large amount of medical and humanistic information and contribute to the development of Chinese medicine. It is a literary style that has been widely accepted and helps to document and disseminate the culture of Chinese medicine. With the advent of big data and information, more Chinese medicine case reports have been published. However, many have insufficient quality to properly guide and apply in the clinical practice, which might be caused by little guidance of Chinese medicine case report standards published. This paper summarized the case report standards, synthesized and appraised the feasibility and problems specific for improving the quality of Chinese medicine case reports, and proposed suggestions and guidance for developing the standardization of Chinese medicine medical case reports.
Adherence to reporting guidelines contributes to report methodology and outcomes of research distinctly and transparently. There are some checklists with specific study types related to surgery on the EQUATOR Network’s website. However, the IDEAL framework focuses on stepwise evaluation of surgical innovation through all stages with some key elements, which those existing guidelines may not mention. This likely results in the inaccuracy in reporting in studies attempting to follow the IDEAL recommendations and suggests a pressing need for IDEAL reporting guidelines. Considering these limitations, the IDEAL developed the IDEAL reporting guidelines between October 2018 and May 2019. The paper aimed to provide interpretation of IDEAL reporting guideline, and promote its understanding and use among Chinese researchers.
Artificial intelligence has been extensively applied in healthcare services recently, and clinical decision support systems driven by artificial intelligence are one of the applications. Early-stage clinical evaluation of artificial intelligence (AI)-based clinical decision support systems lies between preclinical development (in silico), offline validation, and large-scale trials, but few AI-related clinical studies have addressed human factors evaluations and reported the implementation environment, user characteristics, selection process and algorithm identification of AI systems. In order to bridge the development-to-implementation gap in clinical artificial intelligence and to promote the transparent and standardized reporting of early-stage clinical studies of AI-based decision support systems. A reporting guideline for the developmental and exploratory clinical investigations of decision support systems driven by artificial intelligence (DECIDE-AI) was published in 2022. This paper aimed to interpret the background, development process and key items of the DECIDE-AI guideline and promote its understanding as well as dissemination in China.
Effectiveness-implementation hybrid designs can test the effectiveness of interventions and the outcomes of implementation strategies concurrently and accelerate the transformation of research results into routine practice. This paper introduced three types of effectiveness-implementation hybrid designs and corresponding reporting guidelines, including standards for reporting implementation studies, cluster randomized design and CONSORT 2010, stepped-wedge cluster randomized design and extended version CONSORT 2018, qualitative research and COREQ reporting guideline, and provide references for domestic researchers to produce research reports on effectiveness-implementation hybrid design.
The selection of outcome measurement instruments (OMI) is a prerequisite for accurately collecting health outcome data. Conducting a systematic review of OMI can provide an important reference for researchers and practitioners to select the most appropriate OMI. In order to improve the reporting quality of the systematic review of OMI, foreign researchers published the PRISMA-COSMIN reporting guideline in the J Clin Epidemiol in June 2024. This article introduces the research team, development process, and items of the reporting guideline, and analyzes the items with examples. We hope our attempt could help domestic researchers improve the overall quality of OMI systematic reviews.
The surgical case report（SCARE）statement is the report checklist made by European researchers in 2016, which is specialized for surgical case report. As a reference for enhancing the research quality and transparency, the SCARE statement provides a fundamental framework for surgical case reports. The last SCARE statement was revised in 2020, and this paper interprets it to provide a practical tool for domestic researchers in surgical case report.
Dose-response meta-analysis (DRMA) is one of the branches of meta-analysis, which has provided important evidence for clinical research. Since introducing into China, it has gained great attention. In order to improve the reporting quality of DRMA, Dr. Chang Xu et al. developed the reporting guideline for DAMA——G-Dose Checklist. It was published in Chinese Jouranl of Evidence-based Medicine in 2016. This paper interprets the checklists so as to promote the understanding and use of it.
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.
According to the evidence pyramid model, systematic review (SR)/meta-analysis (MA) is one of the essential sources with a high level of clinical evidence. A high-quality SR/MA can effectively guide clinical decision-making and practice. The preferred reporting items for systematic reviews and meta-analyses extension for Chinese herbal medicines (PRISMA-CHM) were officially published in 2020. In this study, based on research cases, the features of PRISMA-CHM were interpreted in detail, so as to help domestic users accurately grasp the details of the reports, in order to improve the quality of the reports of SR/MA of traditional Chinese medicine.