Clinical reasoning is a vital medical education skill, yet its nuances in undergraduate primary care settings remain debated. This systematic review explores clinical reasoning teaching and learning intricacies within primary care. We redefine clinical reasoning as dynamically assimilating and prioritising synthesised patient, significant other, or healthcare professional information for diagnoses or non-diagnoses. This focused meta-synthesis applies transformative learning theory to primary care clinical reasoning education. A comprehensive analysis of 29 selected studies encompassing various designs made insights into clinical reasoning learning dimensions visible. Primary care placements in varying duration and settings foster diverse instructional methods like bedside teaching, clinical consultations, simulated clinics, virtual case libraries, and more. This review highlights the interplay between disease-oriented and patient-centred orientations in clinical reasoning learning. Transformative learning theory provides an innovative lens, revealing stages of initiation, persistence, time and space, and competence and confidence in students' clinical reasoning evolution. Clinical teachers guide this transformation, adopting roles as fortifiers, connoisseurs, mediators, and monitors. Patient engagement spans passive to active involvement, co-constructing clinical reasoning. The review underscores theoretical underpinnings' significance in shaping clinical reasoning pedagogy, advocating broader diversity. Intentional student guidance amid primary care complexities is vital. Utilising transformative learning, interventions bridging cognitive boundaries enhance meaningful clinical reasoning learning experiences. This study contributes insights for refining pedagogy, encouraging diverse research, and fostering holistic clinical reasoning development.
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http://dx.doi.org/10.1080/14739879.2023.2248070 | DOI Listing |
Backgrounds: Biomedical research requires sophisticated understanding and reasoning across multiple specializations. While large language models (LLMs) show promise in scientific applications, their capability to safely and accurately support complex biomedical research remains uncertain.
Methods: We present , a novel question-and-answer benchmark for evaluating LLMs in biomedical research.
IEEE Trans Instrum Meas
May 2024
School of Mechanical Engineering, Shandong University, Jinan 250061, Shandong, China.
Automatic retinal layer segmentation with medical images, such as optical coherence tomography (OCT) images, serves as an important tool for diagnosing ophthalmic diseases. However, it is challenging to achieve accurate segmentation due to low contrast and blood flow noises presented in the images. In addition, the algorithm should be light-weight to be deployed for practical clinical applications.
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Department of Medical Education, College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Caring for critically ill children presents unique challenges due to their rapid deterioration and the need for immediate, complex interventions. The assessment, diagnosis and treatment of deteriorating paediatric patients require a comprehensive and holistic, systematic approach. However, the dynamic nature of critical illness and the need for stabilisation can often lead to missed opportunities for assessment and intervention.
View Article and Find Full Text PDFSudan J Paediatr
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Assistant Professor of Statistics, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Bahrain.
Simulation in medical education improves clinical competence. The Diagnostic Clinical Reasoning Program (DxR), a web-based simulated patient cases software, augments students' clinical skills in a virtual hospital setting. In the Arabian Gulf University, Bahrain, it is used to train medical students before they begin the clinical clerkship.
View Article and Find Full Text PDFJ Community Hosp Intern Med Perspect
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Internal Medicine Residency Program, Florida State University College of Medicine, Tallahassee, FL, USA.
Lymphogranuloma venereum (LGV) is a sexually transmitted infection typically caused by serovars L1-L3 of . These serovars are tissue-invasive with a preponderance for lymphatic spread and can be acquired via unprotected oral, anal, or vaginal sex. We present the case of a 23-year-old with a prior history of syphilis admitted with four weeks of progressively enlarging painful right cervical lymphadenopathy.
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