bluuebunny/crossref_metadata_2025_split huggingface dataset:
Curvilinear relationship between burnout and work engagement among staff in community services for the elderly: A correlation study
Authors: Yang Guangmei , Wei Huiyan , Wan Leping , Dong Haiying , Liang Xiaoxiao , He Yan 
Published: July 2022

ObjectiveTo investigate whether there is a curvilinear relationship between burnout and work engagement among staff in Chinese community services for the elderly.MethodsA stratified whole-group random sampling method was used to survey 244 staff members from eight communities in two cities. Data were collected using the Maslach Burnout Inventory scale (MBI) and the Utrecht Work Engagement Scale- 9 (UWES- 9). The curve estimation method explored the functional model of burnout and work engagement scales.ResultsTwo hundred forty-four staff members completed the survey. Burnout, depersonalization (DP), and personal accomplishment (PA) were found to be related to work engagement in a cubic function (R2 = 0.166, P &lt; 0.05), (R2 = 0.061, P &lt; 0.05), and (R2 = 0.2230, P &lt; 0.05), respectively. There was no statistically significant relationship between emotional exhaustion (EE) and work engagement (P &gt; 0.05). “Personal Accomplishment” is related to work engagement in a U-shaped curve.ConclusionsThere was a cubic function relationship between burnout and work engagement, where “personal accomplishment” was related to work engagement in a U-shaped curve. Therefore, the government and related service organizations should understand the impact of different levels of burnout on work engagement and take targeted measures to alleviate burnout and improve work engagement by targeting emotions and stroke.
Cost-utility analysis of centrally inserted totally implanted access port (PORT) vs. peripherally inserted central catheter (PICC) in the oncology chemotherapy
Authors: Shao Guoliang , Zhou Xiaoying , Zhang Shaoya , Wu Shuaijun , Dong Yichen , Dong Zuojun 
Published: July 2022

BackgroundPeripherally inserted central catheter (PICC) and centrally inserted totally implanted access port (PORT) are two types of intravenous infusion devices that are widely used in clinical practice. PORTs are more expensive to insert than PICCs but have fewer complications. Two cost-utility analyses of PICCs and PORTs in China have been published, but had conflicting findings. This study aimed to compare the cost-utility of PICCs and PORTs.MethodsWe conducted a prospective observational trial including 404 patients with cancer and a cross-sectional study to calculate cost and complications of a PICC and PORT. Utility was measured using the EuroQol five-dimensional questionnaire (EQ-5D-5L). A cost-utility analysis was performed from a healthcare system perspective in China.ResultsThe average total cost of PICCs and PORTs were ¥ 4,091.7 and ¥ 4,566.8, which yielded 0.46 and 0.475 quality-adjusted life-years (QALYs) in a 6-month dwell time, respectively. The incremental cost-utility ratio (ICUR) was ¥ 31,670.9 per QALY. A one-way sensitivity analysis showed that the base-case results were robust, and the probabilistic sensitivity analysis showed that at a willingness-to-pay (WTP) threshold of ¥ 80,976 per QALY (China's per capita GDP in 2021) the probability of a PORT being cost-effective was 96%.ConclusionPORTs were more cost-effective than PICCs for a 6 and 12-month dwell time. The total cost for a PORT was also less than that of a PICC. PORT is therefore recommended as a medium to long-term intravenous delivery device in clinical practice.
Self-care and quality of life among men with chronic heart failure
Authors: Wiśnicka Alicja , Lomper Katarzyna , Uchmanowicz Izabella 
Published: July 2022

IntroductionChronic Heart Failure (CHF) involves a complex regimen of daily self-care behaviors: pharmacological therapy, symptom monitoring and lifestyle modifications. Patients with CHF may have a reduced health related quality of life (HRQoL) due to various physical and emotional symptoms. HRQoL may be improved through the use of self-care interventions.PurposeTo assess the level of self-care and quality of life among men with chronic heart failure.MethodsThe study was conducted among 80 men diagnosed with CHF (mean age 58 years). The study was cross-sectional. A self-administered questionnaire and analysis of medical records were used to collect baseline sociodemographic and clinical data. Self-care was assessed using the standardized European Heart Failure Self-care Behavior Scale- EHFScBS-9 and quality of life was assessed using the World Health Organization Quality of Life Bref.ResultsThe Patients in NYHA class II constituted the vast majority (71.25%), mean LVEF in the study group was 43.5%, and mean disease duration was 3 years. The most common comorbidities were ischemic heart disease (72.5%), hypertension (70%) and diabetes mellitus (60%). The most commonly reported non-pharmacological treatments for NS were fluid restriction (45%), moderate physical activity (42.50%) and daily weight control (41.25%). The EHFSc-9 questionnaire score averaged 50.31 points out of 100 possible (SD = 26.52). The mean score regarding perception of QoL was 2.78 points (SD = 0.91), and 40% of patients indicating poor perception of QoL. The mean score for self-rated Analysis of the results of the individual domains of the WHOQoL BREF questionnaire showed that patients rated their QoL best in the environmental domain (M = 13.28; SD = 3.11), then in the social domain (M = 12.81; SD = 2.71), and in the psychological domain (M = 12.8; SD = 3.2). In contrast, QoL in the physical domain was rated the lowest (M = 10.44; SD = 2.85). There was no significant correlation between quality of life and self-care (p &gt; 0.05).ConclusionsMen with CHF have unsatisfactory self-care outcomes and low quality of life scores and are dissatisfied with their health. Strategies to improve selfcare and quality of life in this group are indicated.
Trends in accessibility of negotiated targeted anti-cancer medicines in Nanjing, China: An interrupted time series analysis
Authors: Liu Yanyan , Yi Huining , Fang Kexin , Bao Yuwen , Li Xin 
Published: July 2022

BackgroundIn order to establish a long-term strategy for bearing the costs of anti-cancer drugs, the state had organized five rounds of national-level pricing negotiations and introduced the National Health Insurance Coverage (NHIC) policy since 2016. In addition, the National Healthcare Security Administration (NHSA) introduced the volume-based purchasing (VBP) pilot program to Nanjing in September 2019. Taking non-small cell lung cancer as an example, the aim of the study was to verify whether national pricing negotiations, the NHIC policy and the VBP pilot program had a positive impact on the accessibility of three targeted anti-cancer drugs.MethodsBased on the hospital procurement data, interrupted time series (ITS) design was used to analyze the effect of the health policy on the accessibility and affordability of gefitinib, bevacizumab and recombinant human endostatin from January 2013 to December 2020 in Nanjing, China.ResultsThe DDDs of the three drugs increased significantly after the policy implementation (P&lt; 0.001,P&lt; 0.001,P= 0.008). The trend of DDDc showed a significant decrease (P&lt; 0.001,P&lt; 0.001,P&lt; 0.001). The mean availability of these drugs before the national pricing negotiation was &lt;30% in the surveyed hospitals, and increased significantly to 60.33% after 2020 (P&lt; 0.001,P= 0.001,P&lt; 0.001). The affordability of these drugs has also increased every year after the implementation of the insurance coverage policy. The financial burden is higher for the rural patients compared with the urban patients, although the gap is narrowing.ConclusionThe accessibility of targeted anti-cancer drugs has increased significantly after the implementation of centralized prices, the NHIC policy and the VBP pilot program, and has shown sustained long-term growth. Multi-pronged supplementary measures and policy approaches by multiple stakeholders will facilitate equitable access to effective and affordable anti-cancer drugs.
Cost-effectiveness of nivolumab plus ipilimumab as first-line treatment for American patients with unresectable malignant pleural mesothelioma
Authors: Ye Zhuo-miao , Tang Zi-Qing , Xu Zhe , Zhou Qin , Li Huan 
Published: July 2022

BackgroundThe treatment paradigm of unresectable malignant pleural mesothelioma (MPM) has changed in recent years. Checkmate 743 demonstrate that nivolumab plus ipilimumab showed good clinical benefits compared with chemotherapy in the treatment of MPM. The study is aim to evaluate the cost-effectiveness of Nivolumab plus ipilimumab vs. platinum plus chemotherapy for the first-line treatment of unresectable MPM.MethodsA Markov model was developed to compare the cost and quality-adjusted life-year (QALY) of nivolumab plus ipilimumab and chemotherapy over a 10-year time horizon. Clinical efficacy and safety data were extracted from the CheckMate 743 trials. Health state utilities were obtained from published literature. Costs were collected from an US payer perspective. One-way and probabilistic sensitivity analyses were conducted to explore the impact of uncertainties on the cost-effectiveness's results.ResultsIn the base case analysis, the incremental healthcare costs and QALYs for Nivolumab plus Ipilimumab vs. chemotherapy are $196,604.22 and 0.53, respectively, resulting an incremental cost-effectiveness ratio (ICER) of $372,414.28/QALYs for the model cohort of patients with locally advanced or metastatic MPM. However, Probabilistic sensitivity analysis showed that there was no probability that Nivolumab plus ipilimumab was cost-effective within the fluctuation range of other model parameters in first-line in unresectable MPM. The results of one-way sensitivity analysis showed that the cost of Nivolumab was the most sensitive parameter.ConclusionsThe ICER of Nivolumab plus ipilimumab is above the theoretical willingness-to-pay threshold in the U.S, which suggests that first-line nivolumab plus ipilimumab for unresectable MPM may be not a cost-effective choice.
Association between body fat percentage and H-type hypertension in postmenopausal women
Authors: Du Shihong , Hong Xiuqin , Yang Yi , Ding Zihao , Yu Tong 
Published: July 2022

BackgroundPrevious studies have explored the relationship between body fat percentage (BFP) and hypertension or homocysteine. However, evidence on the constancy of the association remains inconclusive in postmenopausal women. The aim of this study was to investigate the association between BFP and H-type hypertension in postmenopausal women.MethodsThis cross-sectional study included 1,597 eligible female patients with hypertension. Homocysteine levels ≥10 mmol/L were defined as H-type hypertension. BFP was calculated by measuring patients' physical parameters. Subjects were divided into 4 groups according to quartiles of BFP (Q1: 33.4% or lower, Q2: 33.4–36.1%, Q3: 36.1–39.1%, Q4: &gt;39.1%). We used restricted cubic spline regression models and logistic regression analysis to assess the relationship between BFP and H-type hypertension. Additional subgroup analysis was performed for this study.ResultsAmong 1,597 hypertensive patients, 955 (59.8%) participants had H-type hypertension. There were significant differences between the two groups in age, BMI, educational background, marital status, exercise status, drinking history, WC, TG, LDL, Scr, BUN, and eGFR (P &lt; 0.05). The prevalence of H-type hypertension in the Q1 to Q4 groups was 24.9, 25.1, 24.9, and 25.1%, respectively. After adjusting for relevant factors, we found that the risk of H-type hypertension in the Q4 group had a significantly higher than the Q1 group (OR = 3.2, 95% CI: 1.3–7.5).ConclusionBFP was positively associated with the risk of H-type hypertension in postmenopausal women. Postmenopausal women should control body fat to prevent hypertension.
High Body Mass Index Is Associated with an Increased Risk of the Onset and Severity of Ossification of Spinal Ligaments
Authors: Zhao Yongzhao , Xiang Qian , Lin Jialiang , Jiang Shuai , Li Weishi 
Published: July 2022

BackgroundOssification of the posterior longitudinal ligament (OPLL) and that of ligamentum flavum (OLF) are the main types of the ossification of spinal ligaments (OSL) that cause the thoracic myelopathy. Although several studies have investigated the relationship of body mass index (BMI) with the onset or severity of OSL, it remains unverified due to the contradictory results of existing evidence. A systematic review and meta-analysis were performed in this work to determine the relationship of BMI with the onset and severity of OSL.MethodsPubMed, EMBASE, Web of Science, and Cochrane Library were comprehensively searched online for relevant studies focusing on the relationship of BMI with the onset or severity of the OSL. The difference in BMI of OSL (or severe OSL group) and non-OSL (or nonsevere OSL group) groups was evaluated using the mean difference (MD) with a corresponding 95% confidence interval (CI).ResultsFifteen studies were included in this systematic review and meta-analysis. The BMI of the OSL group was significantly higher than that of the non-OSL group (MD = 1.70 kg/m2, 95% CI = 1.02–2.39 kg/m2, and P &lt; 0.01). Similar results were observed in the subgroup analysis of female (P &lt; 0.01), OPLL (P &lt; 0.01), and OLF (P &lt; 0.01) populations. Three studies reported a significant association of BMI with the ossification index of OSL and the standardized regression coefficient ranging from 0.11 to 0.43 (P &lt; 0.05). Moreover, a significantly higher BMI was observed in the severe OSL group compared with that in the nonsevere OSL group (MD = 3.09, 95% CI, 0.22–5.97 kg/m2, and P = 0.04).ConclusionThe significant association of high BMI with the onset and severity of OSL may provide new evidence and insights into the mechanism research and management of OSL.
Topic identification from news blog in Spanish language
Authors: Pacheco-Guevara Lizbeth , Reátegui Ruth , Valdiviezo-Díaz Priscila 
Published: July 2022

Currently exist a large amount of news in a digital format that need to be classified or labeled automatically according to their content. LDA is an unsupervised technique that automatically creates topics based on words in documents. The present work aims to apply LDA in order to analyze and extract topic from digital news in Spanish language. A total of 198 digital news was collected from a university news blog. A data pre-processing and representation in vector spaces was carried out and k values were selected based on coherence metric. A TF_IDF matrix and a combination of unigrams and bigrams produce topics with a variety of terms and topics related to university activities like study programs, research, projects for innovation and social responsibility. Furthermore, with the manual validation process, terms in topics correspond with hashtags written by the communication professionals.
Blood Service: Efficiency and Safety Improvement
Authors: Дашкевич Э.В. , Климкович Н.Н. 
Published: July 2022

В статье рассмотрено совершенствование службы крови в направлении эффективности и безопасности. Предложены подходы к организации службы крови, управлению запасами и использованию информационных технологий в методических целях.
The article considers the improvement of the blood service in the direction of efficiency and safety. Approaches to the organization of the blood service, stock management and the use of information technology for methodological purposes are proposed.
Automated Detection of Hydrothermal Emission Signatures from Multi-Beam Echo Sounder Images Using Deep Learning
Authors: Mimura Kazuhide , Nakamura Kentaro , Takao Kazuhiro , Yasukawa Kazutaka , Kato Yasuhiro 
Published: July 2022

<p>Seafloor massive sulfide deposits have attracted attention as a mineral resource, as they contain a wide variety of base, precious, and other valuable critical metals. Previous studies have shown that signatures of hydrothermal activity can be detected by a multi-beam echo sounder (MBES), which would be beneficial for exploring sulfide deposits. Although detecting such signatures from acoustic images is currently performed by skilled humans, automating this process could lead to improved efficiency and cost effectiveness of exploration for the seafloor deposits. Herein, we attempted to establish a method for automated detection of MBES water column anomalies using deep learning models. First, we compared the “Mask R-CNN” and “YOLO-v5” detection model architectures, wherein YOLO-v5 yielded higher F1 scores. We then compared the number of training classes and found that models trained with two classes (signal and noise) exhibited superior performance compared with models trained with only one class (signal). Finally, we examined the number of trainable parameters and obtained the best model performance when the YOLO-v5l model with a large trainable parameters was used in the two-class training process. The best model had a precision of 0.928, a recall of 0.881, and an F1 score of 0.904. Moreover, this model achieved a low false alarm rate (less than 0.7%) and had a high detection speed (20−25 ms per frame), indicating that it can be applied in the field for automatic and real-time exploration of seafloor hydrothermal deposits. </p>
<p><br></p>
<p>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</p>
<p><br></p>
<p>2023.2.24: This work was published by IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.</p>
<p>https://ieeexplore.ieee.org/document/10052641</p>
Automated Detection of Hydrothermal Emission Signatures from Multi-Beam Echo Sounder Images Using Deep Learning
Authors: Mimura Kazuhide , Nakamura Kentaro , Takao Kazuhiro , Yasukawa Kazutaka , Kato Yasuhiro 
Published: July 2022

<p>Seafloor massive sulfide deposits have attracted attention as a mineral resource, as they contain a wide variety of base, precious, and other valuable critical metals. Previous studies have shown that signatures of hydrothermal activity can be detected by a multi-beam echo sounder (MBES), which would be beneficial for exploring sulfide deposits. Although detecting such signatures from acoustic images is currently performed by skilled humans, automating this process could lead to improved efficiency and cost effectiveness of exploration for seafloor deposits. Herein, we attempted to establish a method for automated detection of MBES water column anomalies using deep learning models. First, we compared the “Mask R-CNN” and “YOLO-v5” detection model architectures, wherein YOLO-v5 yielded higher F1 scores. We then compared the number of training classes and found that models trained with two classes (signal and noise) exhibited superior performance compared with models trained with only one class (signal). Finally, we examined the number of trainable parameters and obtained the best model performance when the YOLO-v5l model with a large number of trainable parameters was used in the two-class training process. The best model had a precision of 0.928, a recall of 0.881, and an F1 score of 0.904. Using this method, the detection speed was 20−25 ms per frame, which is faster than the pace at which MBES images can generally be generated. Therefore, our best model can be applied in the field for automatic and real-time exploration of seafloor hydrothermal deposits. </p>
<p><br></p>
<p>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</p>
<p><br></p>
Action-Specific Perception & Performance on a Fitts's Law Task in Virtual Reality: The Role of Haptic Feedback
Authors: Kourtesis Panagiotis , Vizcay Sebastian , Marchal Maud , Pacchierotti Claudio , Argelaguet Ferran 
Published: July 2022

<p>While user's perception and performance are predominantly examined independently in virtual reality, the Action-Specific Perception (ASP) theory postulates that the performance of an individual on a task modulates this individual's spatial and time perception pertinent to the task's components and procedures. This paper examines the association between performance and perception and the potential effects that tactile feedback modalities could generate. This paper reports a user study (N=24), in which participants performed a standardized Fitts's law target acquisition task by using three feedback modalities: visual, visuo-electrotactile, and visuo-vibrotactile. The users completed 3 Target Sizes X 2 Distances X 3 feedback modalities = 18 trials. The size perception, distance perception, and (movement) time perception were assessed at the end of each trial. Performance-wise, the results showed that electrotactile feedback facilitates a significantly better accuracy compared to vibrotactile and visual feedback, while vibrotactile provided the worst accuracy. Electrotactile and visual feedback enabled a comparable reaction time, while the vibrotactile offered a substantially slower reaction time than visual feedback. Although amongst feedback types the pattern of differences in perceptual aspects were comparable to performance differences, none of them was statistically significant. However, performance indeed modulated perception. Significant action-specific effects on spatial and time perception were detected. Changes in accuracy modulate both size perception and time perception, while changes in movement speed modulate distance perception. Also, the index of difficulty was found to modulate all three perceptual aspects. However, individual differences appear to affect the magnitude of action-specific effects. These outcomes highlighted the importance of haptic feedback on performance, and importantly the significance of action-specific effects on spatial and time perception in VR, which should be considered in future VR studies. </p>
Injecting Domain Knowledge Into Deep Neural Networks for Tree Crown Delineation
Authors: Harmon Ira , Marconi Sergio , Weinstein Ben , Graves Sarah , Wang Daisy Zhe , Bohlman Stephanie , Zare Alina , Singh Aditya , White Ethan 
Published: July 2022

<p>Automated individual tree crown (ITC) delineation plays an important role in forest remote sensing. Accurate ITC delineation benefits biomass estimation, allometry estimation, and species classification among other forest related tasks, all of which are used to monitor forest health and make important decisions in forest management. In this paper, we introduce Neuro-Symbolic DeepForest, a convolutional neural network (CNN) based ITC delineation algorithm that uses a neuro-symbolic framework to inject domain knowledge (represented as rules written in probabilistic soft logic) into a CNN. We create rules that encode concepts for competition, allometry, constrained growth, mean ITC area, and crown color. Our results show that the delineation model learns from the annotated training data as well as the rules and that under some conditions, the injection of rules improves model performance and affects model bias. We then analyze the effects of each rule on its related aspects of model performance.</p>