Objective: This study aimed to determine whether a combination of case-based learning (CBL) and problem-based learning (PBL) methods in teaching can improve the academic performance and recruitment of medical students for neurosurgery.
Methods: Four classes of fourth-year medical students were randomly divided into two groups. The traditional model group received the traditional teaching method, and the CBL-PBL group received the combined teaching methods of CBL and PBL. After the courses, the differences between the two groups in self-perceived competence, satisfaction with the course, post-class test scores, and clinical practice abilities were compared, and the proportions of neurosurgery major selection in pre- and post-curriculum between the two groups were also analyzed.
Results: Self-perceived competence, post-class test scores, and clinical practice abilities in the CBL-PBL group were better than those in the traditional model group. The students in the CBL-PBL group showed a higher degree of satisfaction with the course than those in the traditional model group (χ2 = 12.03, P = 0.007). At the end of the semester, the proportion of students who chose neurosurgery majors in the CBL-PBL group was 13.3%, more than the 3.4% in the traditional model group (χ2 = 3.93, P = 0.048).
Conclusion: Compared with the traditional teaching method, the CBL and PBL integrated method is more effective for improving the performance of medical students and enhancing their clinical capabilities in neurosurgery teaching. The CBL-PBL method effectively improved students' interests in neurosurgery, potentially contributing to increasing medical student recruitment into neurosurgery.
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http://dx.doi.org/10.1186/s12909-022-03722-y | DOI Listing |
ACS Sens
January 2025
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Solid-phase immunosorbent reactions, such as ELISA, are widely used for detecting, identifying, and quantifying protein markers. However, traditional centimeter scale well-based immunoreactors suffer from low surface-to-volume (S/V) ratios, leading to large sample consumption and a long assay time. Microfluidic technologies, particularly tubular microfluidic immunoreactors, have emerged as promising alternatives due to their high S/V ratios.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China.
To address the susceptibility of conventional vector control systems for permanent magnet synchronous motors (PMSMs) to motor parameter variations and load disturbances, a novel control method combining an improved Grasshopper Optimization Algorithm (GOA) with a variable universe fuzzy Proportional-Integral (PI) controller is proposed, building upon standard fuzzy PI control. First, the diversity of the population and the global exploration capability of the algorithm are enhanced through the integration of the Cauchy mutation strategy and uniform distribution strategy. Subsequently, the fusion of Cauchy mutation and opposition-based learning, along with modifications to the optimal position, further improves the algorithm's ability to escape local optima.
View Article and Find Full Text PDFPLoS One
January 2025
Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, PR. China.
Objectives: The aim of this study was to develop and validate a nomogram model that predicts the risk of bone metastasis (BM) in a prostate cancer (PCa) population.
Methods: We retrospectively collected and analyzed the clinical data of patients with pathologic diagnosis of PCa from January 1, 2013 to December 31, 2022 in two hospitals in Yangzhou, China. Patients from the Affiliated Hospital of Yangzhou University were divided into a training set and patients from the Affiliated Clinical College of Traditional Chinese Medicine of Yangzhou University were divided into a validation set.
PLoS One
January 2025
College of Education for the Future, Beijing Normal University, Zhuhai, Guangdong, China.
Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. This study aims to develop a Generative Adversarial Network (GAN)- based framework to create context-specific training plans by integrating numeric attributes (e.
View Article and Find Full Text PDFDetecting low birth weight is crucial for early identification of at-risk pregnancies which are associated with significant neonatal and maternal morbidity and mortality risks. This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. While traditional approaches to managing class imbalance require labeled data, our study explores the use of unsupervised learning to detect anomalies indicative of low birth weight scenarios.
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