Aims And Objectives: To systematically evaluate the effects of decision aids for women facing breast reconstruction decision on decision conflict, decision regret, knowledge, satisfaction, anxiety and depression.
Background: Breast reconstruction decision is not good or bad and should be guided by clinical evidence and patient preferences. Decision aids can increase the patient's decision-making enthusiasm and ability, improve the quality of decision and promote shared decision-making between patients and medical staff.
Design: Systematic review and meta-analysis.
Methods: Eight databases were conducted from the establishment of the database until October 2021. The PRISMA checklist was selected for analysis in this paper. The meta-analysis was conducted in Review Manager 5.3. The quality of the studies was assessed using the Cochrane risk-of-bias tool. The result is decision conflict, decision regret, knowledge and other secondary outcomes. Sensitivity analysis and subgroup analysis were also conducted.
Results: A total of twelve randomised controlled trials (RCTs) were included in the systematic review and meta-analysis. Meta-analysis revealed that decision aids could significantly reduce decision conflict and decision regret, improve knowledge, satisfaction and depression and had no influence on anxiety.
Conclusions: The results of the systematic review and meta-analysis reviewed the positive effect of decision aids on the decision-making of women facing postmastectomy breast reconstruction. In the future, more well-designed RCTs are needed to confirm the effects of decision aids on the decision-making of breast reconstruction and nurses should be encouraged to take part in the development of decision aids in accordance with strict standards and apply them to breast cancer patients considering postmastectomy breast reconstruction.
Relevance To Clinical Practice: Our study provides evidence for the effectiveness of decision aids on breast reconstruction and points to the important role of healthcare providers in the use of decision aids and in facilitating shared decision-making.
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http://dx.doi.org/10.1111/jocn.16328 | DOI Listing |
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
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January 2025
College of Business, Southern University of Science and Technology, Shenzhen, China.
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions.
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January 2025
Associate Professor, Nursing and Midwifery, University of Limerick, Limerick, Ireland.
Critical thinking is required for successful nursing outcomes. For evidence-based practice, there is a need to understand and apply quantitative methods of research and statistical analysis in order to obtain evidence. However, the literature shows that the use of quantitative methods among nurse researchers can be problematic.
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December 2024
School of Engineering, University of Southern Queensland, Springfield, QLD 4300, Australia.
: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. : Initially, an optimization model is proposed to analyze the impact of making a specific change to an existing case mix, identifying how patient types should be adjusted proportionately to varying levels of hospital resource availability.
View Article and Find Full Text PDFCureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
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