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- Danielsson, D., et al.
(författare)
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Brachytherapy and osteoradionecrosis in patients with base of tongue cancer
- 2023
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Ingår i: Acta Oto-Laryngologica. - : Informa UK Limited. - 0001-6489 .- 1651-2251. ; 143:1, s. 77-84
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Tidskriftsartikel (refereegranskat)abstract
- Background: Base of tongue cancer incidence and patient survival is increasing why treatment sequelae becomes exceedingly important. Osteoradionecrosis (ORN) is a late adverse effect of radiotherapy and brachytherapy (BT) could be a risk factor. Brachytherapy is used in three out of six health care regions in Sweden. Aims: Investigate if patients treated in regions using BT show an increased risk for ORN and whether brachytherapy has any impact on overall survival. Material and Methods: We used data from the Swedish Head and Neck Cancer Register between 2008–2014. Due to the nonrandomized nature of the study and possible selection bias we compared the risk for ORN in brachy vs non-brachy regions. Results: Fifty out of 505 patients (9.9%) developed ORN; eight of these were treated in nonbrachy regions (16%), while 42 (84%) were treated in brachy regions. Neither age, sex, TNM-classification/stage, p16, smoking, neck dissection, or chemotherapy differed between ORN and no-ORN patients. The risk for ORN was significantly higher for patients treated in brachy regions compared to non-brachy regions (HR = 2,63, p =.012), whereas overall survival did not differ (HR = 0.95, p =.782). Conclusions and Significance: Brachytherapy ought to be used cautiously for selected patients or within prospective randomized studies.
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- Seoane, Fernando, et al.
(författare)
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Mean Expected Error in Prediction of Total Body Water: A True Accuracy Comparison between Bioimpedance Spectroscopy and Single Frequency Regression Equations
- 2015
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Ingår i: Biomed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2015:Article ID 656323
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Tidskriftsartikel (refereegranskat)abstract
- For several decades electrical bioimpedance (EBI) has been used to assess body fluid distribution and body composition. Despite the development of several different approaches for assessing total body water (TBW), it remains uncertain whether bioimpedance spectroscopic (BIS) approaches are more accurate than single frequency regression equations. The main objective of this study was to answer this question by calculating the expected accuracy of a single measurement for different EBI methods. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Even the limits of agreement produced from the Bland-Altman analysis indicated that the performance of single frequency, Sun's prediction equations, at population level was close to the performance of both BIS methods; however, when comparing the Mean Absolute Percentage Error value between the single frequency prediction equations and the BIS methods, a significant difference was obtained, indicating slightly better accuracy for the BIS methods. Despite the higher accuracy of BIS methods over 50 kHz prediction equations at both population and individual level, the magnitude of the improvement was small. Such slight improvement in accuracy of BIS methods is suggested insufficient to warrant their clinical use where the most accurate predictions of TBW are required, for example, when assessing over-fluidic status on dialysis. To reach expected errors below 4-5%, novel and individualized approaches must be developed to improve the accuracy of bioimpedance-based methods for the advent of innovative personalized health monitoring applications.
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- Seoane, Fernando, et al.
(författare)
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Slightly superior performance of bioimpedance spectroscopy over single frequency regression equations for assessment of total body water.
- 2015
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Ingår i: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. 25-29 Aug. 2015, Milan, Italy.. - : IEEE. - 1094-687X .- 1558-4615. - 9781424492718 - 9781424492701 ; 2015, s. 3707-10
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Konferensbidrag (refereegranskat)abstract
- Electrical bioimpedance has been used for several decades to assess body fluid distribution and body composition by using single frequency and bioimpedance spectroscopic (BIS) techniques. It remains uncertain whether BIS methods have better performance compare to single frequency regression equations. In this work the performance of two BIS methods and four different 50 kHz single frequency prediction equations was studied in a data set of wrist-to-ankle tetrapolar BIS measurements (5-1000 kHz) together with reference values of total body water obtained by tritium dilution in 92 patients. Data were compared using regression techniques and Bland-Altman plots. The results of this study showed that all methods produced similarly high correlation and concordance coefficients, indicating good accuracy as a method. Limits of agreement analysis indicated that the population level performance of Sun's prediction equations was very similar to the performance of both BIS methods. However, BIS methods in practice have slightly better predictive performance than the single-frequency equations as judged by higher correlation and the limits of agreement from the Bland-Altman analysis. In any case, the authors believe that an accurate evaluation of performance of the methods cannot be done as long as the evaluation is done using Bland-Altman analysis, the commonly accepted technique for this kind of performance comparisons.
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- Abtahi, Yasmine, et al.
(författare)
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Otherness in mathematics education
- 2018
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Ingår i: Proceedings of the 42nd Conference of the International Group for the Psychology of Mathematics Education. - Umeå, Sweden : PME. - 9789176019023 ; , s. 95-124
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Konferensbidrag (refereegranskat)
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- Rad, Mehran, et al.
(författare)
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Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater
- 2024
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Ingår i: Environmental Pollution. - : Elsevier Ltd. - 0269-7491 .- 1873-6424. ; 345
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Tidskriftsartikel (refereegranskat)abstract
- Pentachlorophenol (PCP) is a commonly found recalcitrant and toxic groundwater contaminant that resists degradation, bioaccumulates, and has a potential for long-range environmental transport. Taking proper actions to deal with the pollutant accounting for the life cycle consequences requires a better understanding of its behavior in the subsurface. We recognize the huge potential for enhancing decision-making at contaminated groundwater sites with the arrival of machine learning (ML) techniques in environmental applications. We used ML to enhance the understanding of the dynamics of PCP transport properties in the subsurface, and to determine key hydrochemical and hydrogeological drivers affecting its transport and fate. We demonstrate how this complementary knowledge, provided by data-driven methods, may enable a more targeted planning of monitoring and remediation at two highly contaminated Swedish groundwater sites, where the method was validated. We evaluated 6 interpretable ML methods, 3 linear regressors and 3 non-linear (i.e., tree-based) regressors, to predict PCP concentration in the groundwater. The modeling results indicate that simple linear ML models were found to be useful in the prediction of observations for datasets without any missing values, while tree-based regressors were more suitable for datasets containing missing values. Considering that missing values are common in datasets collected during contaminated site investigations, this could be of significant importance for contaminated site planners and managers, ultimately reducing site investigation and monitoring costs. Furthermore, we interpreted the proposed models using the SHAP (SHapley Additive exPlanations) approach to decipher the importance of different drivers in the prediction and simulation of critical hydrogeochemical variables. Among these, sum of chlorophenols is of highest significance in the analyses. Setting that aside from the model, tetra chlorophenols, dissolved organic carbon, and conductivity found to be of highest importance. Accordingly, ML methods could potentially be used to improve the understanding of groundwater contamination transport dynamics, filling gaps in knowledge that remain when using more sophisticated deterministic modeling approaches.
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