ObjectiveBased on the requirements of the era of big medical data and discipline development, this study aimed to enhance the clinical research capabilities of medical postgraduates by exploring and evaluating some teaching innovations. MethodsA research-oriented clinical research design course was developed for postgraduate students, focusing on enhancing their clinical research abilities. Innovative teaching content and methods were implemented, and a questionnaire survey was conducted to assess the effectiveness of the teaching innovations among clinical medical master's students. ResultsA total of 699 clinical medical master's students completed the survey questionnaire. 94% of students expressed satisfaction with the course, 96% believed that the relevant knowledge covered in the course met the requirements of clinical research, 94% felt that their research capabilities had improved after completing the course, and 99% believed that the course helped them publish academic papers and complete their master's theses. ConclusionStudents recognized the teaching innovations in the course, which stimulated their initiative and enthusiasm for learning, improved the teaching quality of the course, and enhanced the research capabilities of the students.
The use of repeated measurement data from patients to improve the classification ability of prediction models is a key methodological issue in the current development of clinical prediction models. This study aims to investigate the statistical modeling approach of the two-stage model in developing prediction models for non-time-varying outcomes using repeated measurement data. Using the prediction of the risk of severe postpartum hemorrhage as a case study, this study presents the implementation process of the two-stage model from various perspectives, including data structure, basic principles, software utilization, and model evaluation, to provide methodological support for clinical investigators.