| 1. |
- Holmes, G. R., et al.
(författare)
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Cost-Effectiveness Modeling of Surgery Plus Adjuvant Endocrine Therapy Versus Primary Endocrine Therapy Alone in UK Women Aged 70 and Over With Early Breast Cancer
- 2021
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Ingår i: Value in Health. - : Elsevier BV. - 1098-3015. ; 24:6, s. 770-779
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: Approximately 20% of UK women aged 70+ with early breast cancer receive primary endocrine therapy (PET) instead of surgery. PET reduces surgical morbidity but with some survival decrement. To complement and utilize a treatment dependent prognostic model, we investigated the cost-effectiveness of surgery plus adjuvant therapies versus PET for women with varying health and fitness, identifying subgroups for which each treatment is cost-effective. Methods: Survival outcomes from a statistical model, and published data on recurrence, were combined with data from a large, multicenter, prospective cohort study of over 3400 UK women aged 70+ with early breast cancer and median 52-month follow-up, to populate a probabilistic economic model. This model evaluated the cost-effectiveness of surgery plus adjuvant therapies relative to PET for 24 illustrative subgroups: Age {70, 80, 90} × Nodal status {FALSE (F), TRUE (T)} × Comorbidity score {0, 1, 2, 3+}. Results: For a 70-year-old with no lymph node involvement and no comorbidities (70, F, 0), surgery plus adjuvant therapies was cheaper and more effective than PET. For other subgroups, surgery plus adjuvant therapies was more effective but more expensive. Surgery plus adjuvant therapies was not cost-effective for 4 of the 24 subgroups: (90, F, 2), (90, F, 3), (90, T, 2), (90, T, 3). Conclusion: From a UK perspective, surgery plus adjuvant therapies is clinically effective and cost-effective for most women aged 70+ with early breast cancer. Cost-effectiveness reduces with age and comorbidities, and for women over 90 with multiple comorbidities, there is little cost benefit and a negative impact on quality of life. © 2021 ISPOR–The Professional Society for Health Economics and Outcomes Research
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| 2. |
- Irani, I., et al.
(författare)
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Less Than 1% of Core-collapse Supernovae in the Local Universe Occur in Elliptical Galaxies
- 2022
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 927:1
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Tidskriftsartikel (refereegranskat)abstract
- We present observations of three core-collapse supernovae (CCSNe) in elliptical hosts, detected by the Zwicky Transient Facility Bright Transient Survey (BTS). SN 2019ape is a SN Ic that exploded in the main body of a typical elliptical galaxy. Its properties are consistent with an explosion of a regular SN Ic progenitor. A secondary g-band light-curve peak could indicate interaction of the ejecta with circumstellar material (CSM). An Ha-emitting source at the explosion site suggests a residual local star formation origin. SN 2018fsh and SN 2020uik are SNe II which exploded in the outskirts of elliptical galaxies. SN 2020uik shows typical spectra for SNe II, while SN 2018fsh shows a boxy nebular Ha profile, a signature of CSM interaction. We combine these 3 SNe with 7 events from the literature and analyze their hosts as a sample. We present multi-wavelength photometry of the hosts, and compare this to archival photometry of all BTS hosts. Using the spectroscopically complete BTS, we conclude that 0.3%(+0.3)(-0.1) of all CCSNe occur in elliptical galaxies. We derive star formation rates and stellar masses for the host galaxies and compare them to the properties of other SN hosts. We show that CCSNe in ellipticals have larger physical separations from their hosts compared to SNe Ia in elliptical galaxies, and discuss implications for star-forming activity in elliptical galaxies.
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| 3. |
- Vulgarakis Feljan, Aneta, et al.
(författare)
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A Framework for Knowledge Management and Automated Reasoning Applied on Intelligent Transport Systems
- 2017
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Rapport (populärvet., debatt m.m.)abstract
- Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in particular use heterogeneous data sources combined with problem solving expertise in order to make critical decisions that may lead to some form of actions e.g., driver notifications, change of traffic light signals and braking to prevent an accident. Currently, a major part of the decision process is done by human domain experts, which is time-consuming, tedious and error-prone. Additionally, due to the intrinsic nature of knowledge possession this decision process cannot be easily replicated or reused. Therefore, there is a need for automating the reasoning processes by providing computational systems a formal representation of the domain knowledge and a set of methods to process that knowledge. In this paper, we propose a knowledge model that can be used to express both declarative knowledge about the systems’ components, their relations and their current state, as well as procedural knowledge representing possible system behavior. In addition, we introduce a framework for knowledge management and automated reasoning (KMARF). The idea behind KMARF is to automatically select an appropriate problem solver based on formalized reasoning expertise in the knowledge base, and convert a problem definition to the corresponding format. This approach automates reasoning, thus reducing operational costs, and enables reusability of knowledge and methods across different domains. We illustrate the approach on a transportation planning use case.
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