Adaptive and Iterative Learning with Multi-perspective Regularizations for Metal Artifact Reduction.

Metal artifact reduction (MAR) is important for clinical diagnosis with CT images. The existing state-of-the-art deep learning methods usually suppress metal artifacts in sinogram or image domains or both. However, their performance is limited by the inherent characteristics of the two domains, i.e., the errors introduced by local manipulations in the sinogram domain would propagate throughout the whole image during backprojection and lead to serious secondary artifacts, while it is difficult to distinguish artifacts from actual image features in the image domain. To alleviate these limitations, this study analyzes the desirable properties of wavelet transform in-depth and proposes to perform MAR in the wavelet domain. First, wavelet transform yields components that possess spatial correspondence with the image, thereby preventing the spread of local errors to avoid secondary artifacts. Second, using wavelet transform could facilitate identification of artifacts from image since metal artifacts are mainly high-frequency signals. Taking these advantages of the wavelet transform, this paper decomposes an image into multiple wavelet components and introduces multi-perspective regularizations into the proposed MAR model. To improve the transparency and validity of the model, all the modules in the proposed MAR model are designed to reflect their mathematical meanings. In addition, an adaptive wavelet module is also utilized to enhance the flexibility of the model. To optimize the model, an iterative algorithm is developed. The evaluation on both synthetic and real clinical datasets consistently confirms the superior performance of the proposed method over the competing methods.

The role of attention in the effect of facial attractiveness on time perception.

Recent research has indicated that attractive faces often cause a dilation of our time perception thus affecting physical and mental health, and speculates that this could be relevant to the fact that attractive faces capture people's attention. Nevertheless, there was no direct experimental data to support this speculation. The present work was designed to illustrate how attention affects time perception of facial attractiveness. It utilized two experiments to investigate this phenomenon. In Experiment 1, perception of timing and attention bias were assessed using a temporal reproduction task and a dot-probe task. Increased attention bias was found to mediate the time dilation effect of facial attractiveness. Experiment 2 adopted dual-task paradigm, combining a temporal reproduction task and attractiveness rating task, to manipulate attention allocation. The findings suggested that allocating more attention to the task requiring timing enhanced the time dilation effect caused by the faces. Results of Experiments 1 and 2 converge to show that attention plays an essential role in the effects of facial attractiveness on time perception.

Constructing hierarchical attentive functional brain networks for early AD diagnosis.

Analyzing functional brain networks (FBN) with deep learning has demonstrated great potential for brain disorder diagnosis. The conventional construction of FBN is typically conducted at a single scale with a predefined brain region atlas. However, numerous studies have identified that the structure and function of the brain are hierarchically organized in nature. This urges the need of representing FBN in a hierarchical manner for more effective analysis of the complementary diagnostic insights at different scales. To this end, this paper proposes to build hierarchical FBNs adaptively within the Transformer framework. Specifically, a sparse attention-based node-merging module is designed to work alongside the conventional network feature extraction modules in each layer. The proposed module generates coarser nodes for further FBN construction and analysis by combining fine-grained nodes. By stacking multiple such layers, a hierarchical representation of FBN can be adaptively learned in an end-to-end manner. The hierarchical structure can not only integrate the complementary information from multiscale FBN for joint analysis, but also reduce the model complexity due to decreasing node sizes. Moreover, this paper argues that the nodes defined by the existing atlases are not necessarily the optimal starting level to build FBN hierarchy and exploring finer nodes may further enrich the FBN representation. In this regard, each predefined node in an atlas is split into multiple sub-nodes, overcoming the scale limitation of the existing atlases. Extensive experiments conducted on various data sets consistently demonstrate the superior performance of the proposed method over the competing methods.

Multi-channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction.

Score-based generative model (SGM) has risen to prominence in sparse-view CT reconstruction due to its impressive generation capability. The consistency of data is crucial in guiding the reconstruction process in SGM-based reconstruction methods. However, the existing data consistency policy exhibits certain limitations. Firstly, it employs partial data from the reconstructed image of iteration process for image updates, which leads to secondary artifacts with compromising image quality. Moreover, the updates to the SGM and data consistency are considered as distinct stages, disregarding their interdependent relationship. Additionally, the reference image used to compute gradients in the reconstruction process is derived from intermediate result rather than ground truth. Motivated by the fact that a typical SGM yields distinct outcomes with different random noise inputs, we propose a Multi-channel Optimization Generative Model (MOGM) for stable ultra-sparse-view CT reconstruction by integrating a novel data consistency term into the stochastic differential equation model. Notably, the unique aspect of this data consistency component is its exclusive reliance on original data for effectively confining generation outcomes. Furthermore, we pioneer an inference strategy that traces back from the current iteration result to ground truth, enhancing reconstruction stability through foundational theoretical support. We also establish a multi-channel optimization reconstruction framework, where conventional iterative techniques are employed to seek the reconstruction solution. Quantitative and qualitative assessments on 23 views datasets from numerical simulation, clinical cardiac and sheep's lung underscore the superiority of MOGM over alternative methods. Reconstructing from just 10 and 7 views, our method consistently demonstrates exceptional performance.

Spectrum learning for super-resolution tomographic reconstruction.

Objective. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information.Approach. The study proposes a new deep learning based method called spectrum learning (SPEAR) network for CT images super-resolution. This approach leverages both global information in the image domain and high-frequency information in the frequency domain. The SPEAR network is designed to reconstruct high-resolution images from low-resolution inputs by considering not only the main body of the images but also the small structures and other details. The symmetric property of the spectrum is exploited to reduce weight parameters in the frequency domain. Additionally, a spectrum loss is introduced to enforce the preservation of both high-frequency components and global information.Main results. The network is trained using pairs of low-resolution and high-resolution CT images, and it is fine-tuned using additional low-dose and normal-dose CT image pairs. The experimental results demonstrate that the proposed SPEAR network outperforms state-of-the-art networks in terms of image reconstruction quality. The approach successfully preserves high-frequency information and small structures, leading to better results compared to existing methods. The network's ability to generate high-resolution images from low-resolution inputs, even in cases of low-dose CT images, showcases its effectiveness in maintaining image quality.Significance. The proposed SPEAR network's ability to simultaneously capture global information and high-frequency details addresses the limitations of existing methods, resulting in more accurate and informative image reconstructions. This advancement can have substantial implications for various industries and medical diagnoses relying on accurate imaging.

Wavelet-Inspired Multi-channel Score-based Model for Limited-angle CT Reconstruction.

Score-based generative model (SGM) has demonstrated great potential in the challenging limited-angle CT (LA-CT) reconstruction. SGM essentially models the probability density of the ground truth data and generates reconstruction results by sampling from it. Nevertheless, direct application of the existing SGM methods to LA-CT suffers multiple limitations. Firstly, the directional distribution of the artifacts attributing to the missing angles is ignored. Secondly, the different distribution properties of the artifacts in different frequency components have not been fully explored. These drawbacks would inevitably degrade the estimation of the probability density and the reconstruction results. After an in-depth analysis of these factors, this paper proposes a Wavelet-Inspired Score-based Model (WISM) for LA-CT reconstruction. Specifically, besides training a typical SGM with the original images, the proposed method additionally performs the wavelet transform and models the probability density in each wavelet component with an extra SGM. The wavelet components preserve the spatial correspondence with the original image while performing frequency decomposition, thereby keeping the directional property of the artifacts for further analysis. On the other hand, different wavelet components possess more specific contents of the original image in different frequency ranges, simplifying the probability density modeling by decomposing the overall density into component-wise ones. The resulting two SGMs in the image-domain and wavelet-domain are integrated into a unified sampling process under the guidance of the observation data, jointly generating high-quality and consistent LA-CT reconstructions. The experimental evaluation on various datasets consistently verifies the superior performance of the proposed method over the competing method.

Short sleep duration is associated with worse quality of life in Parkinson's disease: A multicenter cross-sectional study.

OBJECTIVE: To characterize sleep duration and investigate its association with quality of life among Parkinson's Disease (PD) patients. METHODS: In this multicenter cross-sectional study, 970 PD patients were divided into five groups based on self-reported sleep duration: <5, ≥5 to <6, ≥6 to <7, ≥7 to ≤8, and >8 h. The quality of life was evaluated using the 39-Item Parkinson's Disease Questionnaire (PDQ-39). Multivariable linear regression analysis, subgroup analysis, and mediation analysis were conducted to examine the association between sleep duration and quality of life. RESULTS: In multivariable linear regression model, patients with sleep duration (<5 h) had significantly higher PDQ-39 scores (ß = 8.132, 95 % CI: 3.99 to 12.266), especially in mobility, activities of daily living, emotional well-being, stigma, social support, cognition, communication, and bodily discomfort (p < 0.05). The association between sleep duration (<5 h) and worse quality of life was more pronounced in patients with higher HY stage, longer disease duration, and sleep disorders. Moreover, a significant indirect effect of sleep duration (<5 h) on quality of life was observed, with UPDRS I, UPDRS II, and UPDRS IV scores acting as mediators. CONCLUSIONS: Short sleep duration (<5 h) is associated with worse quality of life among PD patients. This association was stronger among patients with advanced PD and sleep disorders, while non-motor symptoms and motor complications were identified as significant mediators in this association. These findings highlight the significance of adequate sleep duration and suitable interventions for sleep may help improve quality of life.

Stage-by-stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction.

Diffusion model has emerged as a potential tool to tackle the challenge of sparse-view CT reconstruction, displaying superior performance compared to conventional methods. Nevertheless, these prevailing diffusion models predominantly focus on the sinogram or image domains, which can lead to instability during model training, potentially culminating in convergence towards local minimal solutions. The wavelet transform serves to disentangle image contents and features into distinct frequency-component bands at varying scales, adeptly capturing diverse directional structures. Employing the wavelet transform as a guiding sparsity prior significantly enhances the robustness of diffusion models. In this study, we present an innovative approach named the Stage-by-stage Wavelet Optimization Refinement Diffusion (SWORD) model for sparse-view CT reconstruction. Specifically, we establish a unified mathematical model integrating low-frequency and high-frequency generative models, achieving the solution with an optimization procedure. Furthermore, we perform the low-frequency and high-frequency generative models on wavelet's decomposed components rather than the original sinogram, ensuring the stability of model training. Our method is rooted in established optimization theory, comprising three distinct stages, including low-frequency generation, high-frequency refinement and domain transform. The experimental results demonstrated that the proposed method outperformed existing state-of-the-art methods both quantitatively and qualitatively.

Iterative Residual Optimization Network for Limited-Angle Tomographic Reconstruction.

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems, leading to edge divergence with degraded image quality. Recently, deep learning has been introduced into image reconstruction and achieved great results. However, existing deep reconstruction methods have not fully explored data consistency, resulting in poor performance. In addition, deep reconstruction methods are still mathematically inexplicable and unstable. In this work, we propose an iterative residual optimization network (IRON) for limited-angle tomographic reconstruction. First, a new optimization objective function is established to overcome false negative and positive artifacts induced by limited-angle measurements. We integrate neural network priors as a regularizer to explore deep features within residual data. Furthermore, the block-coordinate descent is employed to achieve a novel iterative framework. Second, a convolution assisted transformer is carefully elaborated to capture both local and long-range pixel interactions simultaneously. Regarding the visual transformer, the multi-head attention is further redesigned to reduce computational costs and protect reconstructed image features. Third, based on the relative error convergence property of the convolution assisted transformer, a mathematical convergence analysis is also provided for our IRON. Both numerically simulated and clinically collected real cardiac datasets are employed to validate the effectiveness and advantages of the proposed IRON. The results show that IRON outperforms other state-of-the-art methods.

Nurses' knowledge of and attitude toward postoperative patient-controlled analgesia (PCA) and the associated factors.

BACKGROUND: Postoperative pain control is pivotal for surgical care; it facilitates patient recovery. Although patient-controlled analgesia (PCA) has been available for decades, inadequate pain control remains. Nurses' knowledge of and attitude toward PCA may influence the efficacy on clinic application. PURPOSE: The purpose of this study is to evaluate nurses' knowledge of and attitude toward postoperative PCA and investigate the associated factors. METHODS: This is a cross-sectional study. We enrolled registered nurses from a 2200-bed medical center in northern Taiwan within one year. The participants completed an anonymous self-reported PCA knowledge inventory and PCA attitude inventory. Data were analyzed descriptively and associated were tested using logistic regression. RESULTS: With 303 participants enrolled, we discovered that nurses had limited knowledge of and a negative attitude toward PCA. Under half of the participants know how to set up a bolus dose and lockout intervals. The majority held misconceptions regarding side effect management for opioids. The minority agree to increase the dose when a patient experienced persistent pain or suggested the use of PCA. Surprisingly, participants with a bachelor's or master's degree had lower knowledge scores than those with a junior college degree. Those with 6-10 years of work experience also are lower than those with under 5 years of experience. However, the participants with experience of using PCA for patient care had higher knowledge scores and a more positive attitude. CONCLUSIONS: Although postoperative PCA has been available for decades and education programs are routinely provided, nurses had limited knowledge of and a negative attitude toward PCA. A higher education level and longer work experience were not associated with more knowledge. The current education programs on PCA should be revised to enhance their efficacy in delivering up-to-date knowledge and situation training which may convey supportive attitude toward clinical application of PCA.

Two-and-a-half order score-based model for solving 3D ill-posed inverse problems.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models demonstrated effectiveness in addressing different inverse problems encountered in the field of CT and MRI, such as sparse-view CT and fast MRI reconstruction. However, these models face challenges in achieving accurate three dimensional (3D) volumetric reconstruction. The existing score-based models predominantly concentrate on reconstructing two-dimensional (2D) data distributions, resulting in inconsistencies between adjacent slices in the reconstructed 3D volumetric images. To overcome this limitation, we propose a novel two-and-a-half order score-based model (TOSM). During the training phase, our TOSM learns data distributions in 2D space, simplifying the training process compared to working directly on 3D volumes. However, during the reconstruction phase, the TOSM utilizes complementary scores along three directions (sagittal, coronal, and transaxial) to achieve a more precise reconstruction. The development of TOSM is built on robust theoretical principles, ensuring its reliability and efficacy. Through extensive experimentation on large-scale sparse-view CT and fast MRI datasets, our method achieved state-of-the-art (SOTA) results in solving 3D ill-posed inverse problems, averaging a 1.56 dB peak signal-to-noise ratio (PSNR) improvement over existing sparse-view CT reconstruction methods across 29 views and 0.87 dB PSNR improvement over existing fast MRI reconstruction methods with × 2 acceleration. In summary, TOSM significantly addresses the issue of inconsistency in 3D ill-posed problems by modeling the distribution of 3D data rather than 2D distribution which has achieved remarkable results in both CT and MRI reconstruction tasks.

Wavelet-Improved Score-Based Generative Model for Medical Imaging.

The score-based generative model (SGM) has demonstrated remarkable performance in addressing challenging under-determined inverse problems in medical imaging. However, acquiring high-quality training datasets for these models remains a formidable task, especially in medical image reconstructions. Prevalent noise perturbations or artifacts in low-dose Computed Tomography (CT) or under-sampled Magnetic Resonance Imaging (MRI) hinder the accurate estimation of data distribution gradients, thereby compromising the overall performance of SGMs when trained with these data. To alleviate this issue, we propose a wavelet-improved denoising technique to cooperate with the SGMs, ensuring effective and stable training. Specifically, the proposed method integrates a wavelet sub-network and the standard SGM sub-network into a unified framework, effectively alleviating inaccurate distribution of the data distribution gradient and enhancing the overall stability. The mutual feedback mechanism between the wavelet sub-network and the SGM sub-network empowers the neural network to learn accurate scores even when handling noisy samples. This combination results in a framework that exhibits superior stability during the learning process, leading to the generation of more precise and reliable reconstructed images. During the reconstruction process, we further enhance the robustness and quality of the reconstructed images by incorporating regularization constraint. Our experiments, which encompass various scenarios of low-dose and sparse-view CT, as well as MRI with varying under-sampling rates and masks, demonstrate the effectiveness of the proposed method by significantly enhanced the quality of the reconstructed images. Especially, our method with noisy training samples achieves comparable results to those obtained using clean data. Our code at https://zenodo.org/record/8266123.

Factors Associated With Levels of Public Engagement in Protective Behaviors During the Early COVID-19 Pandemic: Causal-Comparative Study Based on the Health Belief Model.

BACKGROUND: While the challenges of COVID-19 are still unfolding, the enhancement of protective behavior remains a top priority in global health care. However, current behavior-promoting strategies may be inefficient without first identifying the individuals with lower engagement in protective behavior and the associating factors. OBJECTIVE: This study aimed to identify individuals with and potential contributing factors to low engagement in protective behavior during the COVID-19 pandemic. METHODS: This is a causal-comparative study. A theory-based web-based survey was used to investigate individuals' protective behavior and potential associating factors. During June 2020, the distribution of the survey was targeted to 3 areas: Taiwan, Japan, and North America. Based on the theory of the health belief model (HBM), the survey collected participants' various perceptions toward COVID-19 and a collection of protective behaviors. In addition to the descriptive analysis, cluster analysis, ANOVA, and Fisher exact and chi-square tests were used. RESULTS: A total of 384 responses were analyzed. More than half of the respondents lived in Taiwan, followed by Japan, then North America. The respondents were grouped into 3 clusters according to their engagement level in all protective behaviors. These 3 clusters were significantly different from each other in terms of the participants' sex, residency, perceived barriers, self-efficacy, and cues of action. CONCLUSIONS: This study used an HBM-based questionnaire to assess protective behaviors against COVID-19 and the associated factors across multiple countries. The findings indicate significant differences in various HBM concepts among individuals with varying levels of behavioral engagement.

Pediatric palliative care utilization by decedent children: A nationwide population-based study, 2002-2017.

PURPOSE: This study aimed (1) to describe how trends in pediatric palliative care (PPC) utilization changed from 2002 to 2017, and (2) to examine factors predicting PPC utilization among decedent children in Taiwan. DESIGN: This retrospective, correlational study retrieved 2002-2017 data from three national claims databases in Taiwan. METHODS: Children aged 1 through 18 years who died between January 2002 and December 2017 were included. Pediatric palliative care utilization was defined as PPC enrollment and PPC duration, with enrollment described by frequency (n) and percentage (%) and duration described by mean and standard deviation (SD). Logistic regression was used to examine the associations of various demographic characteristics with PPC enrollment; generalized linear regression was used to examine associations of the demographic characteristics with PPC duration. FINDINGS: Across the 16-year study period, PPC enrollment increased sharply (15.49 times), while PPC duration decreased smoothly (by 29.41%). Cause of death was a continuous predictor of both PPC enrollment and PPC duration. The children less likely to be enrolled in PPC services were those aged 1 to 6 years, boys, living in poverty, living in rural areas, and diagnosed with life-threatening noncancer diseases. CONCLUSION: This study used nationwide databases to investigate PPC enrollment and PPC duration among a large sample of deceased children from 2002 to 2017. The findings not only delineate trends and predictors of PPC enrollment and PPC duration but also highlight great progress in PPC as well as the areas still understudied and underserved. This information could help the pediatric healthcare system achieve the core value of family-centered care for children with life-threatening diseases and their families. CLINICAL RELEVANCE: Pediatric palliative care should be widely and continuously implemented in routine pediatric clinical practice to enhance quality of life for children and their families at the end of life.

Scale Validation of the Mandarin-Language Supportive Care Needs Survey-Adolescent Form.

OBJECTIVES: To (1) modify the Mandarin-language 34-item Supportive Care Needs Survey-Adult Form into the Adolescent Form and (2) examine the psychometric properties of the Adolescent Form. DATA SOURCES: A multiphase, iterative scale validation process was used in this methodological study. Participants who were 13 to 18 years old and receiving cancer treatment in inpatient or outpatient settings, or receiving follow-up care in outpatient settings, were recruited using a convenience sampling method. Confirmatory factor analysis demonstrated good fitness of indices, and all factor loadings for the 18-item Adolescent Form were >0.50, which supported the scale's construct validity. The Adolescent Form score was significantly correlated with the symptom distress score (r = 0.56, P < .01) and quality of life score (r = -0.65, P < .01), which indicated the scale's convergent validity. The correlated item-total correlations (0.30-0.78), Cronbach's alpha (.93), and test-retest reliability coefficient (0.79) confirmed the scale's stability. CONCLUSION: This study successfully modified the 34-item Adult Form into the 18-item Adolescent Form. Given its adequate psychometric properties, this concise scale has great promise as a useful, feasible, and age-appropriate tool for evaluating care needs among adolescents with cancer who speak Mandarin. IMPLICATIONS FOR NURSING PRACTICE: This scale can screen for unmet care needs in busy pediatric oncology settings or large-scale clinical trials. It allows for cross-sectional comparison of unmet care needs between adolescent and adult populations and for longitudinal follow-up into how unmet care needs change from adolescence into adulthood.

Examining the Role of Resilience, Posttraumatic Growth, and Quality of Life in Women with Breast Cancer: A Serial Multiple Mediator Model Approach.

OBJECTIVES: It is unclear how resilience and posttraumatic growth help women with breast cancer face cancer-related symptom distress. This study included both resilience and posttraumatic growth as mediators in a serial multiple mediator model to examine changes in the relationship between symptom distress and quality of life among women with breast cancer. DATA SOURCES: We conducted the descriptive, cross-sectional study in Taiwan. Data were collected using a survey that assessed symptom distress, resilience, posttraumatic growth, and quality of life. A serial multiple mediator model examined one direct and three specific indirect effects of symptom distress on quality of life through resilience and posttraumatic growth. All 91 participants reported the presence of symptom distress and moderate levels of resilience. Quality of life was significantly associated with symptom distress (b = -1.04), resilience (b = 0.18), and posttraumatic growth (b = 0.09). The indirect effect of symptom distress on quality of life through resilience alone was statistically significant (b = -0.23, 95% CI -0.44 to -0.07) and statistically greater than the specific indirect effect through resilience and posttraumatic growth combined (b = -0.21, 95% CI -0.40 to -0.05). CONCLUSION: Resilience plays a unique role in reducing the impact of symptom distress on the quality of life among women with breast cancer. IMPLICATIONS FOR NURSING PRACTICE: Given the importance of resilience to quality of life, oncology nurses can assess the resilience of women with breast cancer and help identify available internal, external, and existential resources to strengthen their resilience.

Noise Suppression With Similarity-Based Self-Supervised Deep Learning.

Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current self-supervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based self-supervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fashion to suppress not only independent but also correlated noises. Theoretically, Noise2Sim is asymptotically equivalent to supervised learning methods under mild conditions. Experimentally, Nosie2Sim recovers intrinsic features from noisy low-dose CT and photon-counting CT images as effectively as or even better than supervised learning methods on practical datasets visually, quantitatively and statistically. Noise2Sim is a general self-supervised denoising approach and has great potential in diverse applications.

Medical clowning influences the emotions of parents of children undergoing cancer treatment: A quasi-experimental study.

BACKGROUND: Medical clowning for children has been found to be effective at enhancing parents' psychological well-being during preoperative preparation, but has not been found during cancer treatment. This study aimed to examine whether and how medical clowning influenced the emotions of parents of children undergoing cancer treatment. METHODS: In this quasi-experimental study, 96 parents of children receiving inpatient cancer treatment were recruited, from June 2018 through April 2020. A demographic questionnaire measuring characteristics of parent and dyadic child, Brief Symptom Rating Scale measuring psychological distress of the parent, and Mood Assessment Scale measuring emotional status of parent and child were administered 1 day before a clowning service. The day after the clowning service, the Mood Assessment Scale again collected emotional status for parent and child. Descriptive analysis, bivariate analysis, and structural equation modeling to fit the actor-partner, cross-lagged model were used. FINDINGS: Parents experienced a low degree of psychological distress that called for emotional management. The indirect effect of medical clowning on parents' emotions through children's emotions was significant, as were the direct effect and total effect of medical clowning on parents' emotions. DISCUSSION: Parents experienced psychological distress during their child's inpatient cancer treatment. Medical clowning can directly improve children's emotions and through this pathway indirectly improve their parents' emotions. APPLICATION TO PRACTICE: There is need to monitor psychological distress and provide interventions for parents of children undergoing cancer treatment. Medical clowns should continue to serve parent-child dyads in pediatric oncology practice and become members of multidisciplinary health care teams.

The impacts of the scope of benefits expansion on hospice care among adult decedents: a nationwide longitudinal observational study.

OBJECTIVES: Compared to aggressive treatment for patients at the end stage of life, hospice care might be more likely to satisfy such patients' need and benefits and improve their dignity and quality of life. Whether the reimbursement policy expansion affect the use of hospice care among various demographics characteristics and health status was unknown. Therefore, the purpose of this study was to explore the impacts of reimbursement policy expansion on hospice care use, and to investigate the effects on people with various demographics characteristics and health status. METHODS: We used the 2001-2017 Taiwan NHI claims data, Death Registry, and Cancer Registry in this study, and we included people who died between 2002 and 2017. The study period was divided into 4 sub-periods. hospice care use and the initiation time of 1st hospice care use were used as dependent variables; demographic characteristics and health status were also collected. RESULTS: There were 2,445,781 people who died in Taiwan during the study period. The results show that the trend of hospice care use increased over time, going steeply upward after the scope of benefits expansion, but the initiation time of 1st hospice care use did not increase after the scope of benefits expansion. The results also show that the effects of expansion varied among patients by demographic characteristics. CONCLUSION: The scope of benefits expansion might induce people's needs in hospice care, but the effects varied by demographic characteristics. Understanding the reasons for the variations in all populations would be the next step for Taiwan's health authorities.