Background: Use of extended pharmacologic thromboprophylaxis after major abdominopelvic cancer surgery should depend on best-available scientific evidence and patients' informed preferences. We developed a risk-stratified patient decision aid to facilitate shared decision-making and sought to evaluate its effect on decision-making quality regarding use of extended thromboprophylaxis.
Methods: We enrolled patients undergoing major abdominopelvic cancer surgery at an academic tertiary care centre in this pre-post study. We evaluated change in decisional conflict, readiness to decide, decision-making confidence, and change in patient knowledge. Participants were provided the appropriate risk-stratified decision aid (according to their Caprini score) in either the preoperative or postoperative setting. A sample size calculation determined that we required 17 patients to demonstrate whether the decision aid meaningfully reduced decisional conflict. We used the Wilcoxon matched-pairs signed ranks test for interval scaled measures.
Results: We included 17 participants. The decision aid significantly reduced decisional conflict (median decisional conflict score 2.37 [range 1.00-3.81] v. 1.3 [range 1.00-3.25], < 0.01). With the decision aid, participants had high confidence (median 86.4 [range 15.91-100]) and felt highly prepared to make a decision (median 90 [range 55-100]). Median knowledge scores increased from 50% (range 0%-100%) to 75% (range 25%-100%).
Conclusion: Our risk-stratified, evidence-based decision aid on extended thromboprophylaxis after major abdominopelvic surgery significantly improved decision-making quality. Further research is needed to evaluate the usability and feasibility of this decision aid in the perioperative setting.
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http://dx.doi.org/10.1503/cjs.014722 | DOI Listing |
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFPLoS One
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.
View Article and Find Full Text PDFBr J Nurs
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.
View Article and Find Full Text PDFHealthcare (Basel)
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 PDFJ Am Heart Assoc
January 2025
Department of Cardiology Beijing Anzhen Hospital, Capital Medical University Beijing China.
Background: Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited.
Methods And Results: A retrospective pooled analysis of individual patient data was performed. Deep-learning-based CT-FFR was calculated.
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