Objective To explore the shortcomings of the traditional clinical probation teaching mode, propose and implement the interactive teaching mode, so as to stimulate the students’ interest in knowledge and achieve better teaching effects. Methods The students of Grade 2017 who had clinical probation in the Orthopaedic Trauma Center of West China Hospital of Sichuan University from September 2020 to December 2021 were selected. Students were randomly divided into traditional clinical probation teaching mode group and interactive teaching mode group according to random number table method. Wechat mini program anonymous questionnaire survey was used to evaluate students’ satisfaction with the interactive teaching model of orthopaedic trauma and the teaching effect. Results A total of 110 students were enrolled, 55 in the traditional clinical probation teaching mode group and 55 in the interactive teaching mode group. There was no significant difference in gender or age between the two groups (P>0.05). The students in the interactive teaching mode group were better than those in the traditional clinical probation teaching mode group in orthopedic theory test (90.13±3.65 vs. 88.39±3.74; t=2.469, P=0.015) in the orthopedic theory test, teacher evaluation (89.15±2.94 vs. 87.56±3.12; t=2.751, P=0.007) and student self-evaluation (89.07±3.18 vs. 87.41±2.89; t=2.865, P=0.005). The teaching satisfaction of the interactive teaching group was higher than that of the traditional teaching group (96.36% vs. 87.27%; Z=−2.159, P=0.031). Conclusion Interactive teaching mode can effectively stimulate students’ interest in knowledge seeking, improve the enthusiasm and interaction of clinical probation, and effectively improve the satisfaction of undergraduate orthopaedic trauma clinical probation teaching.
ObjectiveTo review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. MethodsA comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. ResultsThe rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. ConclusionThe expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.