This is the first of two linked papers exploring decision making in nursing which integrate research evidence from different clinical and academic disciplines. Currently there are many decision-making theories, each with their own distinctive concepts and terminology, and there is a tendency for separate disciplines to view their own decision-making processes as unique. Identifying good nursing decisions and where improvements can be made is therefore problematic, and this can undermine clinical and organizational effectiveness, as well as nurses' professional status. Within the unifying framework of psychological classification, the overall aim of the two papers is to clarify and compare terms, concepts and processes identified in a diversity of decision-making theories, and to demonstrate their underlying similarities. It is argued that the range of explanations used across disciplines can usefully be re-conceptualized as classification behaviour. This paper explores problems arising from multiple theories of decision making being applied to separate clinical disciplines. Attention is given to detrimental effects on nursing practice within the context of multidisciplinary health-care organizations and the changing role of nurses. The different theories are outlined and difficulties in applying them to nursing decisions highlighted. An alternative approach based on a general model of classification is then presented in detail to introduce its terminology and the unifying framework for interpreting all types of decisions. The classification model is used to provide the context for relating alternative philosophical approaches and to define decision-making activities common to all clinical domains. This may benefit nurses by improving multidisciplinary collaboration and weakening clinical elitism.
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Surgery
January 2025
Department of Biomedical Sciences, Humanitas University, Milan, Italy; Department of Hepatobiliary & General Surgery, IRCCS Humanitas Research Hospital, Milan, Italy. Electronic address:
Background: Communicating vessels among hepatic veins in patients with tumors invading/compressing hepatic veins at their caval confluence facilitate new surgical solutions. Although their recognition by intraoperative ultrasound has been described, the possibility of preoperative detection still remains uncertain. We aimed to develop a model to predict their presence before surgery.
View Article and Find Full Text PDFJ Neurosurg
January 2025
Departments of1Neurosurgery.
Objective: Craniopharyngiomas are rare, benign brain tumors that are primarily treated with surgery. Although the extended endoscopic endonasal approach (EEEA) has evolved as a more reliable surgical alternative and yields better visual outcomes than traditional craniotomy, postoperative visual deterioration remains one of the most common complications, and relevant risk factors are still poorly defined. Hence, identifying risk factors and developing a predictive model for postoperative visual deterioration is indeed necessary.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden.
Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes.
View Article and Find Full Text PDFJ Clin Oncol
January 2025
INSERM, IMRBU955, Univ Paris Est Créteil, Créteil, France.
Purpose: Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geriatric predictors. We aim to develop and externally validate a new predictive score (the Geriatric Cancer Scoring System [GCSS]) to refine individualized prognosis for older patients with cancer during the first year after a geriatric assessment (GA).
Materials And Methods: Data were collected from two French prospective multicenter cohorts of patients with cancer 70 years and older, referred for GA: ELCAPA (training set January 2007-March 2016) and ONCODAGE (validation set August 2008-March 2010).
Am J Health Promot
January 2025
College of Social Work, University of South Carolina, Columbia, SC, USA.
Purpose: Artificially Intelligent (AI) chatbots have the potential to produce information to support shared prostate cancer (PrCA) decision-making. Therefore, our purpose was to evaluate and compare the accuracy, completeness, readability, and credibility of responses from standard and advanced versions of popular chatbots: ChatGPT-3.5, ChatGPT-4.
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