Although the current postgraduate education system is gradually improving, there are still some problems in the education mode of postgraduate, especially medical postgraduates in China. The current education mode lacks the ability to stimulate students’ potential to the greatest extent and cultivate talents who can develop comprehensively and focus on a certain direction, and lacks the ability to cultivate “four-faceted” scientific and technological workers who can advance to the breadth and depth of science. In view of the above problems, this paper explores the postgraduate education mode based on the close clinical-basic medicine combination on the basis of the tutor system, and summarizes the successful experience obtained in the process of practical teaching, aiming to provide a high quality reference for the medical postgraduate education.
Objective To explore a new rotation training mode suitable for residency standardized non-professional radiological trainees in radiology department, so as to improve the training quality. Methods The residency standardized non-professional radiological trainees who rotated in the Department of Radiology, West China Hospital, Sichuan University between June 2021 and January 2022 were retrospectively included as the research objects. According to the training mode, they were divided into traditional training mode group and innovative training mode group. The training results of the two groups were compared by taking process assessment, final examination and final score as evaluation indicators. Results Finally, 122 residents were included, including 45 in the traditional training model group and 77 in the innovative training model group. There was no significant difference in gender, major, identity and grade between the two groups (P>0.05). There was no significant difference between the two groups in the first film reading skill examination and their usual homework performance (P>0.05). The score of the second film reading skill examination [15 (14, 16) vs. 12 (11, 13)], the score of the final examination [34 (31, 36) vs. 29 (25, 31)] and the final score [80 (76, 83) vs. 71 (67, 74)] in the innovative training mode group were better than those in the traditional training mode group (P<0.05). Conclusion The innovative training mode of online teaching platform combined with offline teaching can improve the training effect of residency standardized non-professional radiological trainees in radiology department.
For the increasing number of patients with depression, this paper proposes an artificial intelligence method to effectively identify depression through voice signals, with the aim of improving the efficiency of diagnosis and treatment. Firstly, a pre-training model called wav2vec 2.0 is fine-tuned to encode and contextualize the speech, thereby obtaining high-quality voice features. This model is applied to the publicly available dataset - the distress analysis interview corpus-wizard of OZ (DAIC-WOZ). The results demonstrate a precision rate of 93.96%, a recall rate of 94.87%, and an F1 score of 94.41% for the binary classification task of depression recognition, resulting in an overall classification accuracy of 96.48%. For the four-class classification task evaluating the severity of depression, the precision rates are all above 92.59%, the recall rates are all above 92.89%, the F1 scores are all above 93.12%, and the overall classification accuracy is 94.80%. The research findings indicate that the proposed method effectively enhances classification accuracy in scenarios with limited data, exhibiting strong performance in depression identification and severity evaluation. In the future, this method has the potential to serve as a valuable supportive tool for depression diagnosis.