Objective: Our study sought to assess the learning preferences of students studying in the preclinical years of the medical degree program at Oman Medical College, Sohar.
Methods: In this descriptive, cross-sectional study, we administered a learning style questionnaire (VARK model) to 140 students to assess their preferred mode of learning, specifically the sensory modality by which they prefer to take in information.
Results: Over one third (35%) of the respondents expressed their preference for a single mode of learning, either visual (8%), auditory (9%), read/write (9%), or kinesthetic (9%). The remaining students preferred learning using a combination of either two (14%), three (19%), or four (32%) sensory modalities.
Conclusion: The results of our study provide us with useful information to develop appropriate learning approaches to reach all types of learners at the college.
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http://dx.doi.org/10.5001/omj.2014.120 | DOI Listing |
Alzheimers Dement
December 2024
University of California, San Diego, La Jolla, CA, USA.
Background: Neurocognitive health is influenced by multiple modifiable and non-modifiable lifestyle factors. Machine learning tools offer a promising approach to better understand complex models of cognitive function. We used extreme gradient boosting (XG Boost), an algorithm of decision-tree modeling, to analyze the association between 15 late-life lifestyle and demographic factors with episodic memory performance.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka, India.
Background: In the early stages of typical Alzheimer's disease, there is a well-documented pattern of memory deficits, especially episodic memory, substantiated with evidence of medial temporal lobe atrophy, specifically of the hippocampus in line with the memory deficits. Studies have shown that several other demographic, biological, and lifestyle factors influence memory and there is a need for identifying early risk factors and for the development of clinical intervention programs to delay or prevent cognitive decline. Therefore, the objective of this study is to explore the impact of various factors on episodic memory decline in an urban cohort.
View Article and Find Full Text PDFMayo Clin Proc Digit Health
December 2024
School of Computed and Augmented Intelligence, Arizona State University, Tempe, AZ.
Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).
Patients And Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation.
Sci Rep
January 2025
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Anatomical Landmark detection in CT-Scan images is widely used in the identification of skeletal disorders. However, the traditional process of manually detecting anatomical landmarks, especially in three dimensions, is both time-consuming and prone to human errors. We propose a novel, deep-learning-based approach to automatic detection of 3D landmarks in CT images of the lower limb.
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