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- Kjaer, Josefin, et al.
(author)
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Long-term outcome after resection and thermal hepatic ablation of pancreatic neuroendocrine tumour liver metastases
- 2021
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In: BJS open. - : Oxford University Press (OUP). - 2474-9842. ; 5:4
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Journal article (peer-reviewed)abstract
- BACKGROUND: Pancreatic neuroendocrine tumours (Pan-NETs) are rare tumours that often present with or develop liver metastases. The aim of this retrospective study was to evaluate liver surgery and thermal hepatic ablation (THA) of Pan-NET liver metastases and to compare the outcomes with those of a control group. METHOD: Patients with Pan-NET treated in Uppsala University Hospital and Sahlgrenska University Hospital from 1995-2018 were included. Patient records were scrutinized for baseline parameters, survival, treatment and complications. RESULTS: Some 108 patients met the criteria for inclusion; 57 patients underwent treatment with liver surgery or THA and 51 constitute the control group. Median follow-up was 3.93years. Five-year survival in the liver surgery/THA group was 70.6 (95 per cent c.i. 0.57 to 0.84) per cent versus 42.4 (95 per cent c.i. 40.7 to 59.1) per cent in the control group (P=0.016) and median survival was 9.1 (95 per cent c.i. 6.5 to 11.7) versus 4.3 (95 per cent c.i. 3.4-5.2) years. In a multivariable analysis, surgery or THA was associated with a decreased death-years rate (hazard ratio 0.403 (95 per cent c.i. 0.208 to 0.782, P=0.007). CONCLUSION: Liver surgery and/or THA was associated with longer overall survival in Pan-NET with acceptable mortality and morbidity rates. These treatments should thus be considered in Pan-NET patients with reasonable tumour burden in an intent to alleviate symptoms and to improve survival.
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- Muñoz, Adriana, 1967, et al.
(author)
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Decolonizing Ethnographic Databases: A Case Study of Data Migration from the Swedish National Museum of World Culture’s Carlotta to the Open-Source Software Tainacan
- 2022
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Reports (other academic/artistic)abstract
- The report discusses the historical process of building the database currently used by the Swedish National Museums of World Culture (SMVK) in Sweden, called Carlotta. It examines its trajectory and the current problems these museums have been facing to carry out new types of work of documentation and decolonisation of the database. The report then brings focus to the process of extracting and migrating data from Carlotta to a free open-source software called Tainacan, concentrating on a pilot project on the collections of the Amazonian Indigenous group Wai Wai deposited at the SMVK. The results point to new possibilities of use and reuse of information and new possibilities to develop strategies of shared curatorship with the Wai Wai themselves. The report ends by discussing the first results obtained with this initial experiment and raises research questions that need to be deepened in further and more structured stages of the project.
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- Weyuker, Elaine, et al.
(author)
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We’re Finding Most of the Bugs, but What Are We Missing
- 2010
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In: ICST 2010 - 3rd International Conference on Software Testing, Verification and Validation. - 9780769539904 ; , s. 313-322
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Conference paper (peer-reviewed)abstract
- We compare two types of model that have been used to predict software fault-proneness in the next release of a software system. Classification models make a binary prediction that a software entity such as a file or module is likely to be either faulty or not faulty in the next release. Ranking models order the entities according to their predicted number of faults. They are generally used to establish a priority for more intensive testing of the entities that occur early in the ranking. We investigate ways of assessing both classification models and ranking models, and the extent to which metrics appropriate for one type of model are also appropriate for the other. Previous work has shown that ranking models are capable of identifying relatively small sets of files that contain 75-95% of the faults detected in the next release of large legacy systems. In our studies of the rankings produced by these models, the faults not contained in the predicted most faultprone files are nearly always distributed across many of the remaining files; i.e., a single file that is in the lower portion of the ranking virtually never contains a large number of faults.
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