Objective: The aim of this study was to determine the budget impact of everolimus (in combination with letrozole/anastrozole) as a second-line treatment for ER+ HER2- negative advanced and metastatic breast cancer in post-menopausal women.
Research Design And Methods: A cumulative cohort model was developed to estimate the 5-year costs associated with introducing everolimus to the Kazakh healthcare system. Two alternative market share scenarios were compared: with everolimus and without everolimus. PFS and OS data were taken from the trial and extrapolated. The background costs of the pre-progressed and post-progressed health states, drug costs and costs associated with adverse events were included in the model.
Results: The 5-year results from the budget impact analysis demonstrate that the introduction of everolimus leads to a 12% increase in drug costs, a 2% reduction in pre-progression health state costs, a 1% increase in post-progression health state costs, and a 2% reduction in adverse event costs. The net result is only a modest increase in total costs; a 2.69% increase of T201 million, from T7.5 billion to T7.7 billion over a period of 5 years.
Conclusions: The analysis estimated that, if everolimus were to be introduced to the Kazakh healthcare market for the treatment of ER+ HER2- advanced breast cancer, there would be minimal impact upon overall healthcare expenditure. An increase in drug acquisitions costs was almost exactly offset by a reduction in other healthcare costs, due to improved management of the disease.
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http://dx.doi.org/10.3111/13696998.2014.969432 | DOI Listing |
Front Artif Intell
January 2025
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada.
Introduction: Active learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. Active learning is especially useful for semantic segmentation tasks where we can selectively label only a few high-impact regions within these high-impact images. Most established regional active learning algorithms deploy a static-budget querying strategy where a fixed percentage of regions are queried in each image.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
School of Earth Sciences, East China University of Technology, Nanchang, 330013, China.
Investigating the effects of urbanization at the county level on the balance of the carbon budget is essential for progress toward achieving "dual carbon" objectives at the county scale. Based on land use and economic data, this study elucidates the spatiotemporal evolution of urbanization and carbon budget balance ratio in 84 counties in Jiangxi Province from 1980 to 2020. Optimal geographic detectors and geographically weighted random forests were used to explore the impact of urbanization on the carbon budget balance ratio.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
Deep neural networks (DNNs) have demonstrated exceptional performance across various image segmentation tasks. However, the process of preparing datasets for training segmentation DNNs is both labor-intensive and costly, as it typically requires pixel-level annotations for each object of interest. To mitigate this challenge, alternative approaches such as using weak labels (e.
View Article and Find Full Text PDFSci Rep
January 2025
School of Business Administration, Liaoning Technical University, Huludao, 125105, People's Republic of China.
Under the backdrop of frequent emergencies, the rational layout of emergency service facilities (ESF) and the effective allocation of emergency supplies have emerged as crucial in determining the timeliness of post-disaster response. By adequately accounting for potential uncertainties and carrying out comprehensive pre-planning, the robustness of location-allocation decisions can be significantly improved. This paper delves into the ESF network design problem under demand uncertainty and formulates this problem as a two-stage robust optimization model.
View Article and Find Full Text PDFEur J Cancer
January 2025
Division of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:
Background: This study explores the potential of Artificial Intelligence (AI)-generated social media influencers to disseminate cancer prevention messages. Utilizing a Generative AI (GenAI) application, we created a virtual persona, "Wanda", to promote cancer awareness on Instagram.
Methods: We created five posts, addressing the five most modifiable risk factors for cancer: tobacco consumption, unhealthy diet, sun exposure, alcohol consumption, and Human Papillomavirus (HPV) infection.
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