Introduction: There is much subjective discussion, but few empirical data that explore how students approach the learning of anatomy.
Aims: Students' perceptions of successful approaches to learning anatomy were correlated with their own approaches to learning, quality of learning and grades.
Methods: First-year medical students (n = 97) studying anatomy at an Australian university completed an online survey including a version of the Study Process Questionnaire (SPQ) that measures approaches to learning. The quality of students' written assessment was rated using the Structure of Observed Learning Outcomes (SOLO) taxonomy. Final examination data were used for correlation with approaches and quality of learning.
Results: Students perceived successful learning of anatomy as hard work, involving various combinations of memorisation, understanding and visualisation. Students' surface approach (SA) scores (mean 30 +/- 3.4) and deep approach (DA) scores (mean 31 +/- 4.2) reflected the use of both memorisation and understanding as key learning strategies in anatomy. There were significant correlations between SOLO ratings and DA scores (r = 0.24, P < 0.01), between SA scores and final grades (r = - 0.30, P < 0.01) and between SOLO ratings and final grades (r = 0.61, P < 0.01) in the subject.
Conclusions: Approaches to learning correlate positively with the quality of learning. Successful learning of anatomy requires a balance between memorisation with understanding and visualisation. Interrelationships between these three strategies for learning anatomy in medicine and other disciplines require further investigation.
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http://dx.doi.org/10.1111/j.1365-2929.2006.02643.x | DOI Listing |
Clin Exp Med
January 2025
Department of Thoracic Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China.
Introduction Recently, immune cells within the tumor microenvironment (TME) have become crucial in regulating cancer progression and treatment responses. The dynamic interactions between tumors and immune cells are emerging as a promising strategy to activate the host's immune system against various cancers. The development and progression of hepatocellular carcinoma (HCC) involve complex biological processes, with the role of the TME and tumor phenotypes still not fully understood.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.
View Article and Find Full Text PDFJ Neurochem
January 2025
Center for Protein Diagnostics (PRODI) Biospectroscopy, Ruhr University Bochum, Bochum, Germany.
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-beta (Aβ) plaques in the brain, contributing to neurodegeneration. This study investigates lipid alterations within these plaques using a novel, label-free, multimodal approach. Combining infrared (IR) imaging, machine learning, laser microdissection (LMD), and flow injection analysis mass spectrometry (FIA-MS), we provide the first comprehensive lipidomic analysis of chemically unaltered Aβ plaques in post-mortem human AD brain tissue.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurosurgery, University Hospital Tübingen, Tübingen, Germany.
To compare 1D (linear) tumor volume calculations and classification systems with 3D-segmented volumetric analysis (SVA), focusing specifically on their effectiveness in the evaluation and management of NF2-associated vestibular schwannomas (VS). VS were clinically followed every 6 months with cranial, thin-sliced (< 3 mm) MRI. We retrospectively reviewed and used T1-weighted post-contrast enhanced (gadolinium) images for both SVA and linear measurements.
View Article and Find Full Text PDFPLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
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