OBJECTIVE: Research on sociocultural factors associated caregiver-provider relationship is needed to enhance family involvement in psychiatric care. This study examines from the caregiver's perspective the associations of schizophrenia attributions, stigmatization, and caregiving experiences with caregiver-psychiatrist working relationship in Taiwan. METHODS: This cross-sectional study used a convenience sample of 152 Taiwanese family caregivers of persons diagnosed with schizophrenia, recruited from a grassroots organization, 4 community mental health rehabilitation centers and 2 psychiatric hospitals between July 2012 and March 2013. Multiple linear regression models were used for analysis. RESULTS: Biological attribution was positively associated with perceived family collaboration, and so was environmental attribution with perceived informational support. Internalized stigma was negatively associated with perceived family collaboration. Caregiving rewards were positively related to both perceived family collaboration and informational support, and so was experience of problems with services to perceived family collaboration. CONCLUSION: The examination of family perceptions informs Western psychiatric care providers of the importance of culturally sensitive practices in developing an effective working relationship with family caregivers, particularly in regards to caregivers' casual attributions, impact of stigma, and caregiving experiences.
Caregivers ; Cooperative Behavior ; Cross-Sectional Studies ; Hospitals, Psychiatric ; Humans ; Linear Models ; Mental Health ; Rehabilitation Centers ; Reward ; Schizophrenia ; Stereotyping ; Taiwan*

Aged, 80 and over ; Carcinoma, Transitional Cell ; diagnostic imaging ; pathology ; Humans ; Male ; Tomography, X-Ray Computed ; Urinary Bladder Neoplasms ; diagnostic imaging ; pathology

Purpose: The purpose of this study was to assess the amount of variance in the coping strategies of patients with brain tumors that could be accounted for by resilience. Methods: This cross-sectional survey involved 95 patients who had experienced surgical, chemotherapy, or radiotherapy therapies for their brain tumors at least 1 month before data collection. The investigator collected data using the scales of the Ways of Coping Checklist-Revised and Resilience Scale. Data were analyzed using descriptive statistics, t tests, analysis of variance, Pearson product–moment correlation, and hierarchical multiple regression. Results: The results revealed that resilience was significantly positively associated with patients' problem-focused coping (r = .65, p < .001) and total coping (r = .49, p < .001). In addition, resilience accounted for 27% (R2inc = .27, p < .001) and 16% ((R2inc = .16, p < .001) of the distinct variances in predicting patients’ problem-focused coping and total coping. Conclusion The current results provide evidence to support the importance of resilience in shaping the coping strategies of relevant patients. As resilience shows a crucial element in patient coping with brain tumors, health team members should develop and employ appropriate strategies to improve the resilience of patients with brain tumors.

Purpose: The purpose of this study was to assess the amount of variance in the coping strategies of patients with brain tumors that could be accounted for by resilience. Methods: This cross-sectional survey involved 95 patients who had experienced surgical, chemotherapy, or radiotherapy therapies for their brain tumors at least 1 month before data collection. The investigator collected data using the scales of the Ways of Coping Checklist-Revised and Resilience Scale. Data were analyzed using descriptive statistics, t tests, analysis of variance, Pearson product–moment correlation, and hierarchical multiple regression. Results: The results revealed that resilience was significantly positively associated with patients' problem-focused coping (r = .65, p < .001) and total coping (r = .49, p < .001). In addition, resilience accounted for 27% (R2inc = .27, p < .001) and 16% ((R2inc = .16, p < .001) of the distinct variances in predicting patients’ problem-focused coping and total coping. Conclusion The current results provide evidence to support the importance of resilience in shaping the coping strategies of relevant patients. As resilience shows a crucial element in patient coping with brain tumors, health team members should develop and employ appropriate strategies to improve the resilience of patients with brain tumors.

BACKGROUND/AIMS: Only moderate to severe Crohn's Disease (CD) patients without a satisfactory conventional therapy effect are eligible to get reimbursement from the National Health Insurance of Taiwan for using adalimumab. These are more stringent criteria than in many Western countries and Japan and Korea. We aim to explore the efficacy of using adalimumab in CD patients under such stringent criteria. METHODS: A retrospective analysis was conducted in nine medical centers in Taiwan and we collected the results of CD patients receiving adalimumab from Sep 2009 to Mar 2014. The clinical characteristics, response measured by CDAI (Crohn's Disease Activity Index), adverse events and survival status were recorded and analyzed. CR-70, CR-100, and CR-150 were defined as attaining a CDAI decrease of 70, 100 or 150 points compared with baseline. RESULTS: A total of 103 CD patient records were used in this study. Sixty percent of these patients received combination therapy of adalimumab together with immunomodulators. CR-70 was 68.7%, 74.5% and 88.4% after week 4, 8 and 12 of treatment, respectively. The steroid-free rate, complications and survival were 47.6%, 9.7% and 99% of patients, respectively. In considering the mucosal healing, only 25% patients achieve mucosal healing after treatment for 6 to12 months. Surgery was still needed in 16.5% of patients. Combination treatment of adalimumab with immunomodulators further decreased the level of CDAI at week 8 when compared with the monotherapy. CONCLUSIONS: Even under the stringent criteria for using adalimumab, the response rate was comparable to those without stringent criteria.
Adalimumab ; Crohn Disease* ; Humans ; Immunologic Factors ; Inflammatory Bowel Diseases* ; Japan ; Korea ; National Health Programs ; Retrospective Studies ; Taiwan*

Background/Aims: Obstacles exist in facilitating hepatitis C virus (HCV) care cascade. To increase timely and accurate diagnosis, disease awareness and accessibility, in-hospital HCV reflex testing followed by automatic appointments and a late call-back strategy (R.N.A. model) was applied. We aimed to compare the HCV treatment rate of patients treated with this strategy compared to those without. Methods: One hundred and twenty-five anti-HCV seropositive patients who adopted the R.N.A. model in 2020 and another 1,396 controls treated in 2019 were enrolled to compare the gaps in accurate HCV RNA diagnosis to final treatment allocation. Results: The HCV RNA testing rate was significantly higher in patients who received reflex testing than in those without reflex testing (100% vs. 84.8%, P<0.001). When patients were stratified according to the referring outpatient department, a significant improvement in the HCV RNA testing rate was particularly noted in patients from non-hepatology departments (100% vs. 23.3%, P<0.001). The treatment rate in HCV RNA seropositive patients was 83% (83/100) after the adoption of the R.N.A. model, among whom 96.1% and 73.9% of patients were from the hepatology and non-hepatology departments, respectively. Compared to subjects without R.N.A. model application, a significant improvement in the treatment rate was observed for patients from non-hepatology departments (73.9% vs. 27.8%, P=0.001). The application of the R.N.A. model significantly increased the in-hospital HCV treatment uptake from 6.4% to 73.9% for patients from non-hepatology departments (P<0.001). Conclusions The care cascade increased the treatment uptake and set up a model for enhancing in-hospital HCV elimination.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.