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Background. General Practice/Family Medicine includes approaches to the biological, technological, behavioural, sociological and anthropological domains. Objectives. To document the domains addressed in the final assignments of the Integrated Master's Degree in Medicine at the Faculty of Medicine, University of Coimbra, in the area of GP/FM. Material and methods. Observational study of the titles of final assignments, between 2008 and 2017, granted by the Faculty of Medicine of the University of Coimbra. A domain analysis using as codes the International Classification in Primary Care-2 and the Q-Codes, a context classification in Primary Care, year of elaboration and gender of author was carried out for each title of final assignment. A descriptive and inferential analysis was performed through parametric and nonparametric tests. Results. 169 papers were analysed, 23.1% written by male students, with a positive overall growth dynamics (Δ = +7) between 2008 and 2017. Q-Codes were registered 276 times, while the ICPC-2 codes were used 133 times. Under the Q-Codes, "doctor's issues" is the most frequently addressed (n = 112; 67.2%), and under the International Classification in Primary Care-2 classifications, the chapter "Psychological" was predominant (n = 35; 21%). Under the Q-Codes, subcategories "primary care setting" (n = 26; 15.6%), "health issue management" (n = 23; 13.8%) and "unable to code, unclear" (22; 13.2%) were dominant. Within the International Classification in Primary Care-2, the subcategories "diabetes noninsulin dependent" (n = 22; 13.2%), "depressive disorder" (8, 4.8%) and "hypertension uncomplicated" (8; 4.8%) were predominantly focused on. Conclusions. The 3CGP may become a professional tool, allowing for more precise identification of final works, for a better communication method in medical activity and for avoiding the loss of previously developed works. [ABSTRACT FROM AUTHOR]

Over the last two decades, augmented reality (AR) has led to the rapid development of new interfaces in various fields of social and technological application domains. One such domain is medicine, and to a higher extent surgery, where these visualization techniques help to improve the effectiveness of preoperative and intraoperative procedures. Following this trend, this paper presents SLIMBRAIN, a real-time acquisition and processing AR system suitable to classify and display brain tumor tissue from hyperspectral (HS) information. This system captures and processes HS images at 14 frames per second (FPS) during the course of a tumor resection operation to detect and delimit cancer tissue at the same time the neurosurgeon operates. The result is represented in an AR visualization where the classification results are overlapped with the RGB point cloud captured by a LiDAR camera. This representation allows natural navigation of the scene at the same time it is captured and processed, improving the visualization and hence effectiveness of the HS technology to delimit tumors. The whole system has been verified in real brain tumor resection operations. [ABSTRACT FROM AUTHOR]