Zobrazení zdroje stránky Krausevoss0121

inally published in JMIR Medical Informatics (http//medinform.jmir.org), 27.02.2020.BACKGROUND Internet-delivered cognitive behavior therapy (ICBT) has been found to be effective for treating alcohol misuse in research trials, but it is not available as part of routine care in Canada. Recent recommendations in the literature highlight the importance of integrating perspectives from both patient and health care stakeholders when ICBT is being implemented in routine practice settings. OBJECTIVE This study aimed to gain an understanding of how ICBT is perceived as a treatment option for alcohol misuse by interviewing diverse stakeholders. Specifically, the objectives were to (1) learn about the perceived advantages and disadvantages of ICBT for alcohol misuse and (2) elicit recommendations to inform implementation efforts in routine practice. METHODS A total of 30 participants representing six stakeholder groups (ie, patients, family members, academic experts, frontline managers, service providers, and health care decision makers) participated in semistructured interviews. To be included in the; appeal to the diverse residents of Saskatchewan; and use language that is simple, encouraging, and nonjudgmental. Finally, in terms of population characteristics, stakeholders felt that several features of the alcohol misuse population, such as psychiatric comorbidity, readiness for change, and stigma, should be considered when developing an ICBT program for alcohol misuse. CONCLUSIONS Stakeholders' insights will help maximize the acceptability, appropriateness, and adoption of ICBT for alcohol misuse and in turn contribute to implementation success. The methodology and findings from this study could be of benefit to others who are seeking to implement ICBT in routine practice. ©Heather D Hadjistavropoulos, Kirsten M Gullickson, Shelley Adrian-Taylor, Andrew Wilhelms, Christopher Sundström, Marcie Nugent. Originally published in JMIR Mental Health (http//mental.jmir.org), 03.03.2020.BACKGROUND Dry eye disease (DED) is a complex disease of the ocular surface, and its associated factors are important for understanding and effectively treating DED. OBJECTIVE This study aimed to provide an integrative and personalized model of DED by making an explanatory model of DED using as many factors as possible from the Korea National Health and Nutrition Examination Survey (KNHANES) data. METHODS Using KNHANES data for 2012 (4391 sample cases), a point-based scoring system was created for ranking factors associated with DED and assessing patient-specific DED risk. First, decision trees and lasso were used to classify continuous factors and to select important factors, respectively. Next, a survey-weighted multiple logistic regression was trained using these factors, and points were assigned using the regression coefficients. Finally, network graphs of partial correlations between factors were utilized to study the interrelatedness of DED-associated factors. RESULTS The point-based model achieved an area under the curve of 0.70 (95% CI 0.61-0.78), and 13 of 78 factors considered were chosen. Important factors included sex (+9 points for women), corneal refractive surgery (+9 points), current depression (+7 points), cataract surgery (+7 points), stress (+6 points), age (54-66 years; +4 points), rhinitis (+4 points), lipid-lowering medication (+4 points), and intake of omega-3 (0.43%-0.65% kcal/day; -4 points). Among these, the age group 54 to 66 years had high centrality in the network, whereas omega-3 had low centrality. CONCLUSIONS Integrative understanding of DED was possible using the machine learning-based model and network-based factor analysis. This method for finding important risk factors and identifying patient-specific risk could be applied to other multifactorial diseases. ©Sang Min Nam, Thomas A Peterson, Atul J Butte, Kyoung Yul Seo, Hyun Wook Han. Originally published in JMIR Medical Informatics (http//medinform.jmir.org), 20.02.2020.BACKGROUND Computer-aided diagnosis (CAD) is used as an aid tool by radiologists on breast lesion diagnosis in ultrasonography. Previous studies demonstrated that CAD can improve the diagnosis performance of radiologists. However, the optimal use of CAD on breast lesions according to size (below or above 2 cm) has not been assessed. OBJECTIVE The aim of this study was to compare the performance of different radiologists using CAD to detect breast tumors less and more than 2 cm in size. METHODS We prospectively enrolled 261 consecutive patients (mean age 43 years; age range 17-70 years), including 398 lesions (148 lesions&gt;2 cm, 79 malignant and 69 benign; 250 lesions≤2 cm, 71 malignant and 179 benign) with breast mass as the prominent symptom. One novice radiologist with 1 year of ultrasonography experience and one experienced radiologist with 5 years of ultrasonography experience were each assigned to read the ultrasonography images without CAD, and then again at a second reading while applying the CAD S-Deteinese Clinical Trial Registry ChiCTR1800019649; http//www.chictr.org.cn/showprojen.aspx?proj=33094. ©Liang Yongping, Ping Zhou, Zhang Juan, Zhao Yongfeng, Wengang Liu, Yifan Shi. Originally published in JMIR Medical Informatics (http//medinform.jmir.org), 02.03.2020.BACKGROUND Privacy restrictions limit access to protected patient-derived health information for research purposes. Consequently, data anonymization is required to allow researchers data access for initial analysis before granting institutional review board approval. [https://www.selleckchem.com/products/raptinal.html Raptinal] A system installed and activated at our institution enables synthetic data generation that mimics data from real electronic medical records, wherein only fictitious patients are listed. OBJECTIVE This paper aimed to validate the results obtained when analyzing synthetic structured data for medical research. A comprehensive validation process concerning meaningful clinical questions and various types of data was conducted to assess the accuracy and precision of statistical estimates derived from synthetic patient data. METHODS A cross-hospital project was conducted to validate results obtained from synthetic data produced for five contemporary studies on various topics. For each study, results derived from synthetic data were compared with those based on real data.