Background: Minimal access surgery and, lately, single-incision laparoscopic procedures are challenging and demanding with regard to the skills of the surgeon performing the procedures. This article presents the results of an investigation of the performance and attention focus of 21 medical interns and surgical residents training in an immersive context. That is, training 'in situation', representing more realistically the demands imposed on the surgeons during minimal access surgery.
Methods: Twenty-one medical interns and surgical residents participated in simulation trainings in an integrated operating room for laparoscopic surgery. Various physiological measures of body heat expenditure were gathered as indicators of mental strain and attention focus.
Results: The results of the Mann-Whitney test indicated that participants with a poor performance in the two laparoscopic cholecystectomy cases had a significantly (U = 3, p = 0.038) higher heat flux at the start of the procedure (mean 107.08, standard deviation [SD] 24.34) than those who excelled in the two cases (mean 62.64, SD 23.41). Also, the average frontal head temperature of the participants who failed at the task was significantly lower (mean 33.27, SD 0.52) than those who performed well (mean 33.92, SD 0.27).
Conclusions: Surgeons cannot operate in a bubble; thus, they should not be trained in one. Combining heat flux and frontal head temperature could be a good measure of deep involvement and attentional focus during performance of simulated surgical tasks.
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http://dx.doi.org/10.1007/s00464-013-3350-4 | DOI Listing |
JBI Evid Synth
January 2025
School of Nursing and Midwifery, University of Newcastle, Newcastle, New South Wales, Australia.
Objective: The objective of this review was to synthesize the available evidence on the experiences of African women who migrated to a developed country and encountered intimate partner violence (IPV).
Introduction: IPV is a significant public health issue, and migrant women living in developed countries are particularly vulnerable to IPV, experiencing disproportionately higher rates of IPV. Understanding the experiences of these women can inform health policy and decision-making in clinical practice to minimize IPV.
Curr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
BMJ Open
December 2024
Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
Objective: Patients with heart failure (HF) perform a variety of self-care activities to control symptoms and minimise the risk of HF decompensations. The objective of this study was to understand how patients build capacity and manage the work of living with HF.
Design: A qualitative study using semi-structured telephone interviews.
BMJ Support Palliat Care
December 2024
National Children's Bureau, London, UK.
Background: Efforts to minimise inequity in palliative and end-of-life care (PEoLC) are well-researched. This is frequently explained by differences related to singular factors. The concept of intersectionality recognises that the combination of variables exacerbates disparities.
View Article and Find Full Text PDFEur J Hum Genet
January 2025
Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Artificial intelligence (AI) has been growing more powerful and accessible, and will increasingly impact many areas, including virtually all aspects of medicine and biomedical research. This review focuses on previous, current, and especially emerging applications of AI in clinical genetics. Topics covered include a brief explanation of different general categories of AI, including machine learning, deep learning, and generative AI.
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