ArticleBMC medical education2025
Dual-track drive for precision education: developing a targeted teaching model in the standardized training of lymphoma subspecialty physicians through the integration of problem-based learning and case-based learning.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Evaluation of the combined PBL-CBL teaching method based on Thorndike's Learning Theory in the education of bacterial resistance detection.Frontiers in medicine · 2026Article
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Authors and funding
4 authors.
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Abstract
In the training of lymphoma subspecialty physicians, confronted with the challenges of rapid knowledge updates and complex diagnosis and treatment decisions, this paper proposes a dual-track teaching model that integrates problem-based learning (PBL) and case-based learning (CBL). This model prompts trainees to actively explore knowledge through well-designed clinical problem chains and simulates the clinical decision-making process using selected real cases, aiming to enhance trainees' independent diagnosis and treatment capabilities, critical thinking, and evidence-based decision-making skills. The teaching implementation emphasizes teacher guidance, group collaboration, and multidimensional ability assessment. This model can effectively stimulate learning motivation and promote the integration of clinical thinking and interdisciplinary knowledge. In the future, it is necessary to continuously optimize teacher capabilities, update case banks, and explore the application of new technologies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.