Objective: The aim of this research was to explore (1) clinical years students' perceptions about radiology case-based learning within a computer supported collaborative learning (CSCL) setting, (2) an analysis of the collaborative learning process, and (3) the learning impact of collaborative work on the radiology cases.
Methods: The first part of this study focuses on a more detailed analysis of a survey study about CSCL based case-based learning, set up in the context of a broader radiology curriculum innovation. The second part centers on a qualitative and quantitative analysis of 52 online collaborative learning discussions from 5th year and nearly graduating medical students. The collaborative work was based on 26 radiology cases regarding musculoskeletal radiology.
Results: The analysis of perceptions about collaborative learning on radiology cases reflects a rather neutral attitude that also does not differ significantly in students of different grade levels. Less advanced students are more positive about CSCL as compared to last year students. Outcome evaluation shows a significantly higher level of accuracy in identification of radiology key structures and in radiology diagnosis as well as in linking the radiological signs with available clinical information in nearly graduated students. No significant differences between different grade levels were found in accuracy of using medical terminology.
Conclusion: Students appreciate computer supported collaborative learning settings when tackling radiology case-based learning. Scripted computer supported collaborative learning groups proved to be useful for both 5th and 7th year students in view of developing components of their radiology diagnostic approaches.
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http://dx.doi.org/10.1016/j.ejrad.2010.08.041 | DOI Listing |
J Clin Med
December 2024
Anesthesiology and Operative Intensive Care, Faculty of Medicine, University of Augsburg, 86156 Augsburg, Germany.
Mediastinal mass syndrome represents a major threat to respiratory and cardiovascular integrity, with difficult evidence-based risk stratification for interdisciplinary management. We conducted a narrative review concerning risk stratification and difficult airway management of patients presenting with a large mediastinal mass. This is supplemented by a case report illustrating our individual approach for a patient presenting with a subtotal tracheal stenosis due to a large cyst of the thyroid gland.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China.
To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft-vehicle-airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle-airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method.
View Article and Find Full Text PDFNurse Educ Pract
January 2025
Nursing and Health School, Zhengzhou University, Zhengzhou, Henan Province, PR China. Electronic address:
Aim: To translate, culturally adapt and evaluate the psychometric properties of the Peer Evaluation Scale for Team-based Learning (PES-TBL) for students in nursing and medical disciplines.
Background: Effective peer evaluation tools provide a more scientific and objective assessment of collaborative learning. However, there is a lack of peer evaluation instruments designed for group learning in China.
J Am Vet Med Assoc
January 2025
This review focuses on opportunities and challenges of future AI developments in veterinary medicine, from the perspective of computer science researchers in developing AI systems for animal behavior analysis. We examine the paradigms of supervised learning, self-supervised learning, and foundation models, highlighting their applications and limitations in automating animal behavior analysis. These emerging technologies present future challenges in data, modeling, and evaluation in veterinary medicine.
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