MARC

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022 |a 1472-6920 
024 7 |a 10.1186/s12909-025-07566-0  |2 doi 
035 |a 3227642776 
045 2 |b d20250101  |b d20251231 
084 |a 58506  |2 nlm 
100 1 |a Hong, Wei 
245 1 |a Research on the construction and application of pathology knowledge graph 
260 |b Springer Nature B.V.  |c 2025 
513 |a Journal Article 
520 3 |a BackgroundDigital transformation in pathology education faces three bottlenecks: fragmented knowledge transfer, low morphological diagnostic accuracy, and weak clinical reasoning. While knowledge graphs (KGs) offer potential solutions, existing medical KG lack multimodal integration and competency assessment. We designed an integrated Multimodal Knowledge Graph (MKG) with O-PIRTAS pedagogy to bridge these gaps.MethodsFollowing Design Science Research Methodology, we built a pathology-specific MKG featuring: (1) Semantic modeling of disease mechanisms (etiology-pathogenesis-morphology-clinical), (2) Cross-modal alignment of digital slides/animations/clinical cases, (3) Embedded metrics (KII/MDA/CCAE) for competency quantification. A quasi-experiment with 533 medical students (2022 cohort control: n = 275; 2023 MKG-O-PIRTAS: n = 258) evaluated outcomes via exam scores, validated questionnaires, and stratified interviews.ResultsThe MKG-O-PIRTAS group achieved significantly higher adjusted exam scores (76.14 vs. 73.72, p = 0.033) and 86% lower misdiagnosis rate in high performers (p = 0.015). Cognitive load diverged markedly (57.5 vs. 75.5, p = 0.007), with high performers dynamically contextualizing MKG nodes into clinical reasoning, while novices required scaffolded pathways. Over 80% of students endorsed enhanced knowledge integration and process optimization.ConclusionThe MKG-O-PIRTAS artifact transforms scattered pathology knowledge into actionable clinical reasoning scaffolds, proving effective for personalized competency development. Future work will scale adaptive scaffolding and integrate real-time EMR modules, establishing a replicable paradigm for medical education intelligence. 
653 |a Teaching 
653 |a Medical education 
653 |a Coding theory 
653 |a Questionnaires 
653 |a Educational technology 
653 |a Cognition & reasoning 
653 |a Efficiency 
653 |a Etiology 
653 |a Digital transformation 
653 |a Flipped classroom 
653 |a Knowledge 
653 |a Quasi-experimental methods 
653 |a Learning 
653 |a Semantics 
653 |a Design of experiments 
653 |a Clinical medicine 
653 |a Ontology 
653 |a Medical students 
653 |a Pathology 
653 |a Interviews 
653 |a Use statistics 
653 |a Data collection 
653 |a Morphology 
653 |a Instructional scaffolding 
653 |a Instructional Improvement 
653 |a Atlases 
653 |a Knowledge Representation 
653 |a Medical Evaluation 
653 |a Instructional Effectiveness 
653 |a Course Content 
653 |a Learner Engagement 
653 |a Constructivism (Learning) 
653 |a Methods Research 
653 |a Educational Strategies 
653 |a Algorithms 
653 |a Educational Resources 
653 |a Control Groups 
653 |a Influence of Technology 
653 |a Learning Theories 
653 |a Intelligence 
653 |a Data Analysis 
700 1 |a Liu, Xue 
700 1 |a Cao, Huiling 
700 1 |a Qin, Weiqi 
700 1 |a Ma, Qun 
700 1 |a Kong, Lingling 
773 0 |t BMC Medical Education  |g vol. 25 (2025), p. 1-12 
786 0 |d ProQuest  |t Healthcare Administration Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3227642776/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3227642776/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3227642776/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch