Objective: To compare the value of SF36v2 versus multi-attribute utility score (MAS) for predicting treatment outcome in heavy menstrual bleeding (HMB).
Study Design: Longitudinal observational study, in an outpatient service of a large UK teaching hospital. 193 women took part. Women were asked to complete SF36v2 and a multi-attribute utility score (MAS) for menorrhagia before the first consultation. Patient management was determined through an evidence based guideline and blind to their response to the questionnaire. Treatment outcome at 8 months was examined in relation to the physical (PCS) and mental (MCS) health summary scales of SF36v2 and to MAS.
Results: At study entry equal numbers of patients, 179 (93%), returned usable responses for SF36v2 and the multi-attribute scale; 178 (92%) returned both. Baseline SF36v2 scores for role physical, bodily pain, social functioning and mental health were significantly lower (p<0.05) for the group of women who finally required surgery, but the difference in PCS or MCS was not statistically significant. The mean MAS score for those who did not need surgery was 50.7, and for those who needed surgery following failed medical treatment was 35.06. The difference was statistically significant (p<0.001, 95% CI 7.47-23.82). Using logistic regression analysis there was a statistically significant association between baseline MAS but not MCS or PCS and the need for surgery. However, there was considerable overlap between treatment groups.
Conclusions: MAS may be a better predictor of management outcome compared to SF36v2 for HMB; but its utility for the individual patient is limited.
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http://dx.doi.org/10.1016/j.ejogrb.2009.09.021 | DOI Listing |
Sci Rep
December 2024
State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, China.
Addressing the need to harmonize environment conservation and sustainable economic development within the Yellow River Basin (YRB) requires a profound comprehension of the spatiotemporal dynamics of urban ecosystem resilience. This study developed an index system utilizing the resistance-adaptability-recovery framework to measure these dynamics. By applying the advanced multi-attribute boundary area comparison method and a spatial autocorrelation model, we investigated the spatiotemporal variations and spatial correlation patterns of urban ecological resilience across the YRB.
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December 2024
Department of Computer Engineering, Faculty of Engineering and Industrial Technology, Kalasin University, Kalasin 46000, Thailand.
The utilization of agricultural waste for producing charcoal briquettes is gaining significant attention as a sustainable alternative energy source. Converting these residues into charcoal briquettes not only addresses energy shortages but also provides an efficient solution for managing agricultural waste, contributing to environmental sustainability. This study proposes a novel methodology integrating a Data Envelopment Analysis Variant (DEAV) with the Best-Worst Method (BWM) to assess and rank biomass materials for charcoal briquette production.
View Article and Find Full Text PDFHeliyon
July 2024
Department od Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic.
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management.
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October 2024
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran.
Congenital heart disease (CHD) remains a significant global health concern, affecting approximately 1 % of newborns worldwide. While its accurate causes often remain elusive, a combination of genetic and environmental factors is implicated. In this cross-sectional study, we propose a comprehensive prediction framework leveraging Machine Learning (ML) and Multi-Attribute Decision Making (MADM) techniques to enhance CHD diagnostics and forecasting.
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October 2024
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
Linguistic term fuzzy sets provide an intuitive way to express preferences, enhancing understanding and communication among decision-makers. In this article, we introduce the novel concept of p,q-quasirung orthopair fuzzy linguistic sets (p,q-QOFLSs), which merge the principles of p,q-quasirung orthopair fuzzy sets (p,q-QOFSs) with linguistic fuzzy sets. This new framework offers a more robust approach to handle uncertain and imprecise information in decision-making processes, characterized by linguistic membership and non-membership degrees.
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