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- Bertoglio, Sergio, et al.
(författare)
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Improving outcomes of short peripheral vascular access in oncology and chemotherapy administration
- 2017
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Ingår i: Journal of Vascular Access. - : Wichtig Publishing. - 1129-7298 .- 1724-6032. ; 18:2, s. 89-96
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Tidskriftsartikel (refereegranskat)abstract
- A short peripheral intravenous catheter or cannula (PIVC) is frequently used to deliver chemotherapy in oncology practice. Although safe and easy to insert, PIVCs do fail, leading to personal discomfort for patients and adding substantially to treatment costs. As the procedure of peripheral catheterization is invasive, there is a need for greater consistency in the choice, insertion and management of short PIVCs, particularly in the oncology setting where there is a growing trend for patients to receive many different courses of IV treatment over a number of years, sometimes with only short remissions. This article reviews best practice with respect to PIVCs in cancer patients and considers the necessity for bundling these actions. Two care bundles, addressing both insertion and ongoing care and maintenance, are proposed. These have the potential to improve outcomes with the use of short PIVCs for vascular access in oncology practice.
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- Magallon, D, et al.
(författare)
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European expert network for the reduction of uncertainties in severe accident safety issues (EURSAFE)
- 2005
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Ingår i: Nuclear Engineering and Design. - : Elsevier BV. - 0029-5493 .- 1872-759X. ; 235:2-4, s. 309-346
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Tidskriftsartikel (refereegranskat)abstract
- EURSAFE thematic network was a concerted action in the sixth framework programme of the European Commission. It established a large consensus among the main actors in nuclear safety on the severe accident issues where large uncertainties still subsist. The conclusions were derived from a first-of-kind phenomena identification and ranking tables (PIRT) on all aspects of severe accident also realised in the frame of the project. Starting from a list of all severe accident phenomena containing approximately 1000 entries and established by the twenty partner organisations, 106 phenomena were retained eventually as both important for safety and still lacking sufficient knowledge. Ultimately, 21 research areas for addressing these phenomena regrouped according to their similarities were identified. A networking structure for implementing and executing the necessary research was proposed, which promotes integration and harmonisation of the different national programmes. A severe accident database structure was proposed to ensure preservation of experimental data and enhanced communication for data exchange and use for severe accident codes assessment. The final product, named EURSAFE, is a website network, http://asa2.jrc.it/eursafe, connecting nodes located at partner sites. As the result of an action involving R&D governmental institutions, regulatory bodies, nuclear industry, utilities and universities from six EU Member States (Finland, France, Germany, Spain, Sweden, UK) plus JRC, three European third countries (Czech Republic, Hungary, Switzerland), and USA, EURSAFE represents a significant step towards harmonisation and credibility of the approaches, and resolution of the remaining severe accident issues.
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- Nemlander, E., et al.
(författare)
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A machine learning tool for identifying non-metastatic colorectal cancer in primary care
- 2023
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Ingår i: European Journal of Cancer. - : Elsevier BV. - 0959-8049 .- 1879-0852. ; 182, s. 100-106
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Tidskriftsartikel (refereegranskat)abstract
- Background: Primary health care (PHC) is often the first point of contact when diagnosing colorectal cancer (CRC). Human limitations in processing large amounts of information warrant the use of machine learning as a diagnostic prediction tool for CRC. Aim: To develop a predictive model for identifying non-metastatic CRC (NMCRC) among PHC patients using diagnostic data analysed with machine learning. Design and setting: A case-control study containing data on PHC visits for 542 patients >18 years old diagnosed with NMCRC in the Vastra Gotaland Region, Sweden, during 2011, and 2,139 matched controls. Method: Stochastic gradient boosting (SGB) was used to construct a model for predicting the presence of NMCRC based on diagnostic codes from PHC consultations during the year before the date of cancer diagnosis and the total number of consultations. Variables with a normalised relative influence (NRI) >1% were considered having an important contribution to the model. Risks of having NMCRC were calculated using odds ratios of marginal effects. Results: Of the 361 variables used as predictors in the stochastic gradient boosting model, 184 had non-zero influence, with 16 variables having NRI >1% and a combined NRI of 63.3%. Variables representing anaemia and bleeding had a combined NRI of 27.6%. The model had a sensitivity of 73.3% and a specificity of 83.5%. Change in bowel habit had the highest odds ratios of marginal effects at 28.8. Conclusion: Machine learning is useful for identifying variables of importance for predicting NMCRC in PHC. Malignant diagnoses may be hidden behind benign symptoms such as haemorrhoids. 2023 The Author(s). Published by Elsevier Ltd.
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