Objective: To gain caregivers' insights into the decision-making process in dementia patients with regard to treatment and care.
Methods: Four focus group interviews (n=29).
Results: The decision-making process consists of three elementary components: (1) identifying an individual's needs; (2) exploring options; and (3) making a choice. The most important phase is the exploration phase as it is crucial for the acceptance of the disease. Furthermore, the decision is experienced more as an emotional choice than a rational one. It is influenced by personal preferences whereas practical aspects do not seem to play a substantial role.
Conclusion: Several aspects make decision-making in dementia different from decision-making in the context of other chronic diseases: (1) the difficulty accepting dementia; (2) the progressive nature of dementia; (3) patient's reliance on surrogate decision-making; and (4) strong emotions. Due to these aspects, the decision-making process is very time-consuming, especially the crucial exploration phase.
Practice Implications: A more active role is required of both the caregiver and the health care professional especially in the exploration phase, enabling easier acceptance and adjustment to the disease. Acceptance is an important condition for reducing anxiety and resistance to care that may offer significant benefits in the future.
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http://dx.doi.org/10.1016/j.pec.2011.07.023 | DOI Listing |
Prz Gastroenterol
September 2024
Department of Surgery, General University Hospital of Patras, Patras, Greece.
Artificial intelligence (AI) and image processing are revolutionising the diagnosis and management of liver cancer. Recent advancements showcase AI's ability to analyse medical imaging data, like computed tomography scans and magnetic resonance imaging, accurately detecting and classifying liver cancer lesions for early intervention. Predictive models aid prognosis estimation and recurrence pattern identification, facilitating personalised treatment planning.
View Article and Find Full Text PDFIndian J Radiol Imaging
January 2025
Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom.
Any scientific journal of repute constantly strives to ensure the highest possible quality, integrity, and ethical standards of published research. This article attempts to the highlight multifaceted responsibilities of an Editor in Chief (EiC) and editors such as managing the peer review process, detecting plagiarism, and ensuring quality of selected manuscript before publication. The EiC also has to tackle issues of salami slicing, duplicate submissions, secondary publications, and guest and ghost authorship while adhering to constantly evolving guidelines of the International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE).
View Article and Find Full Text PDFMany inherited metabolic disorders (IMD) are associated with end-organ damage necessitating organ transplantation. Although utilization of deceased donors with history of IMD warrants caution, there may be circumstances under which such donors could be considered as suitable organ donor candidates. We present the first known report of liver transplantation from a deceased donor with cystinosis.
View Article and Find Full Text PDFJ Med Ultrasound
February 2024
Department of Otolaryngology, Taipei Medical University Hospital, Taipei, Taiwan.
Sialolith-induced obstructive sialadenitis is a commonly encountered clinical scenario, yet the variations in the size and location of the stone can complicate immediate clinical assessment. Utilizing dynamic ultrasound imaging along with specific structural markers can provide valuable, immediate objective evidence in diagnosing submandibular sialolithiasis. This initial ultrasound evaluation streamlines the decision-making process by facilitating the timely scheduling of confirmatory computed tomography scans and guiding subsequent surgical interventions.
View Article and Find Full Text PDFActas Esp Psiquiatr
January 2025
Department of Clinical Laboratory, Ningbo No.2 Hospital, 315099 Ningbo, Zhejiang, China.
Background: Accurate diagnosis and classification of Alzheimer's disease (AD) are crucial for effective treatment and management. Traditional diagnostic models, largely based on binary classification systems, fail to adequately capture the complexities and variations across different stages and subtypes of AD, limiting their clinical utility.
Methods: We developed a deep learning model integrating a dot-product attention mechanism and an innovative labeling system to enhance the diagnosis and classification of AD subtypes and severity levels.
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