Traditional Scale Research Articles

Idebenone does not inhibit disability progression in primary progressive MS

BackgroundMultiple sclerosis (MS) is a chronic, immune-mediated neurodegenerative disorder of the central nervous system (CNS). While current MS therapies target the inflammatory processes, no treatment explicitly targets mitochondrial dysfunction and resulting axonal loss. Therefore, the aim of this study was to determine whether idebenone inhibits mitochondrial dysfunction and accumulation of disability in primary progressive MS (PPMS) and to enhance understanding of pathogenic mechanisms of PPMS progression using cerebrospinal fluid (CSF) biomarkers. MethodsThe double-blind, placebo-controlled Phase I/II clinical trial of Idebenone in patients with Primary Progressive MS (IPPoMS; NCT00950248) was an adaptively designed, baseline-versus-treatment, placebo-controlled, CSF-biomarker-supported trial. Based on interim analysis of the 1-year pre-treatment data, change in the area under the curve of Combinatorial Weight-Adjusted Disability Score (CombiWISE) became the primary outcome, with >80% power to detect ≥40% efficacy with 28 patients/arm treated for 2 years in baseline versus treatment paradigm. Changes in traditional disability scales and in brain ventricular volume were secondary outcomes. Exploratory outcomes included CSF biomarkers of mitochondrial dysfunction (Growth/differentiation factor 15 [GDF15] and lactate), axonal damage (neurofilament light chain [NFL]), innate immunity (sCD14), blood brain barrier leakage (albumin quotient) and retinal nerve fiber layer thinning. ResultsIdebenone was well tolerated but did not inhibit disability progression or CNS tissue destruction. Concentrations of GDF15, secreted predominantly by astrocytes and choroid plexus epithelium in vitro, increased after exposure to mitochondrial toxin rotenone, validating the ability of this biomarker to measure intrathecal mitochondrial damage. CSF GDF15 levels correlated strongly with age and MS patients had CSF levels of GDF15 significantly above age-adjusted healthy volunteers, with highest levels measured in PPMS. Idebenone did not change CSF GDF15 levels. ConclusionMitochondrial dysfunction exceeding normal aging reflected by age-adjusted CSF GDF15 is present in the majority of PPMS patients, but it is not inhibited by idebenone.

Intelligent Computing: Knowledge Acquisition Method Based on the Management Scale Transformation

Abstract The widespread scale effect always generates significant changes in the properties or characteristics of management objects with different observation scales. Thus, this paper studies the scale transformation mechanism problem of management objects. The observation scale hierarchy (management scale) with clear management objectives could automatically be recognized through changing the observation scales, in order to improve the practical management efficiency. Firstly, an intelligent computing framework based on the scale transformation is established, which reduces the over-dependency of human involvement in traditional scale transformation methods. Then, the scale characteristic reasoning inference is put forward to improve the knowledge acquisition mechanism of scale transformation. Finally, a knowledge acquisition algorithm based on the variable-scale clustering (KAVSC) is proposed. Experiments selected the multiple products inventory data of a manufacturing enterprise from 1 January 2015 to 31 December 2017. The experiment results illustrate that the proposed algorithm KAVSC is able to accurately recognize different management scale levels and scale characteristics of each product, which could effectively support managers making differentiated inventory management plans.

An Adaptive CPU-GPU Governing Framework for Mobile Games on big.LITTLE Architectures

Games have been one of the most popular applications on smartphones. In order to meet the increasing computational complexity of mobile games, smartphones are now equipped with heterogeneous CPU multi-core architectures like big.LITTLE as well as high-performance GPUs. However, the integrated CPUs and GPUs drain the battery quickly, which has become a bottleneck for improving user experience. In addition to traditional Dynamic Voltage and Frequency Scaling (DVFS) technique for CPUs and GPUs power reduction, heterogeneous multi-core processors, such as the big.LITTLE architecture, have been designed to offer more opportunity for performance-energy tradeoffs. But current processor governors in smartphones can not exploit these power-saving mechanisms wisely, causing considerable energy waste. In this article, we propose a CPU-GPU governing framework that recognizes the performance demand for different game scenes, and select the most energy-efficient hardware configuration for the corresponding scenes. We implement our framework on an ODROID-XU4 mobile platform, and the experiments show that our framework can achieve 26.7, 16.6, and 10.5 percent power saving on average without compromising user experience when compared to the default governor used in our platform and two governors proposed by other researchers, respectively.

Are the ‘Illnesses’ of Traditional Likert Scales Treatable?

Purpose: The main aim of this paper is to introduce the development and the application of a fuzzy rating scale in measuring customer satisfaction which are to be demonstrated through a healthcare example in order to illustrate how the proposed methodology is able to enhance the reliability of traditional Likert scale-based evaluations.Methodology/Approach: The proposed methodology is built on fuzzy sets the membership function of which is composed of two sigmoid functions by applying Dombi’s conjunction operator. The possible ‘values’ of the linguistic variable expressing customer satisfaction are to be expressed by these functions which can also be linked to the level of organizational performance allowing the illustration of the mainly nonlinear relationship between the provided and perceived service performance.Findings: The application of the proposed fuzzy rating scale confirms its ability to reflect the unambiguity of human ratings as well as the context-dependency of ratings resulting in a more precise representation of human judgements.Research Limitation/implication: The presented methodology may be viewed as a viable approach in any kind of service quality evaluations where Likert-type scales are traditionally applied to handle its weaknesses.Originality/Value of paper: The proposed methodology is not only able to reflect the satisfaction of customers and the organizational performance simultaneously, but the expectations of customers related to the desired level of performance can also be incorporated into the establishment of the scale yielding to more reliably supported managerial decisions.

A Longitudinal Comparison of Patient-Reported Outcomes Measurement Information System to Legacy Scales in Knee and Shoulder Arthroscopy Patients

The purpose of this prospective correlational study was to compare the psychometric properties of the Defense and Veterans Pain Rating Scale (DVPRS) and Patient-Reported Outcomes Measurement Information System (PROMIS) scales with those of traditional legacy scales over a longitudinal postoperative period in patients undergoing arthroscopic procedures. Active-duty military personnel undergoing shoulder and knee arthroscopic procedures completed the DVPRS, PROMIS, and legacy scales preoperatively and again at 3 months postoperatively. Rolling correlation coefficients (rrm) were used to assess the concurrent validity between the DVPRS average pain scores and computer adaptive testing PROMIS scales (Anxiety, Depression, Pain Behavior, Pain Interference, Mobility, Sleep Disturbance, Satisfaction With Social Roles, and Upper Extremity Physical Function) and the legacy scales (American Shoulder and Elbow Surgeons [ASES] scale, Shoulder Activity Scale, International Knee Documentation Committee [IKDC] questionnaire, Single Assessment Numeric Evaluation [SANE], Marx Activity Rating Scale, and Veterans RAND 12-Item Health Survey). A total of 121 participants completed the scales preoperatively, 80 completed them at 3months postoperatively, and 59 (49% of participants) completed them at both times. Rolling correlation coefficients between PROMIS Pain Interference (rrm= -0.41, P= .006), Satisfaction With Social Roles (rrm= 0.56, P < .001), and Upper Extremity (rrm=0.71, P < .001) scores showed acceptable concurrent validity with ASES scores over a longitudinal postoperative period. DVPRS scores (rrm= -0.69, P= .002) and PROMIS Pain Behavior (rrm= -0.57, P= .018), Pain Interference (rrm= -0.71, P= .001), Sleep Disturbance (rrm= -0.64, P= .005), and Mobility (rrm= 0.65, P= .005) scores showed acceptable concurrent validity with IKDC scores. There was poor correlation between the PROMIS and DVPRS scores and the Single Assessment Numeric Evaluation, Marx Activity Rating Scale, Shoulder Activity Scale, and Veterans RAND 12-Item Health Survey scores. PROMIS scales measuring physical function, pain presentation, and other health domains showed acceptable concurrent validity with ASES and IKDC scores. By integrating the prospective collection of biopsychosocial PROMIS scales into practice, it is possible for clinicians in orthopaedic settings to assess changes in validated patient-reported outcomes to inform patient-centered care planning throughout the postoperative recovery period. Level II, prospective comparative study (with not all participants completing follow-up).

Numerical Investigation on the Optimum Thermal Design of the Shape and Geometric Parameters of Microchannel Heat Exchangers with Cavities.

Due to the large surface-area-to-volume ratio, microchannel heat exchangers have a higher heat transfer rate compared with traditional scale heat exchangers. In this study, the optimum microchannel cavity with high heat transfer and low flow resistance is designed to further improve microchannel exchangers’ thermal performance. A three-dimensional laminar flow model, consisting of Navier–Stokes equations and an energy conservation equation is solved and the conjugate heat transfer between the silicon basement and deionized water is taken into consideration. The impact of the shape, aspect ratio, size and spacing of the cavity on the thermal performance of microchannel exchangers are numerically investigated, respectively. The results indicated that the cavity on the sidewall can enhance heat transfer and reduce flow resistance simultaneously, and cavities with a relatively small expansion angle and streamlined edge could enhance thermal performance the most. Based on the conclusions, a new cavity shape is proposed, and the simulation results verify its excellent thermal performance as expected. Furthermore, investigation is performed to figure out the optimum design of the new cavity and the optimal geometric parameters of the cavity under different flow conditions have been obtained in principle for microchannel exchangers’ design.

Feasibility and usability of MentorPal, a framework for rapid development of virtual mentors

One-on-one mentoring is effective for helping novices with career development. However, traditional mentoring scales poorly. To address this problem, MentorPal emulates conversations with a panel of virtual mentors based on recordings of real STEM professionals. Students freely ask questions as they might in a career fair, while machine learning algorithms respond with best-match answers. MentorPal is researching rapid development of new virtual mentors, where training data will be sparse. In a usability study, 31 high school students reported (a) increased career knowledge and confidence, (b) positive ease-of-use, and that (c) mentors were helpful (87%) but seldom covered their preferred career (29%). These results demonstrate feasibility for virtual mentoring, but efficacy studies are needed to evaluate its impact, particularly for groups with limited STEM opportunities.

Robust and precise isotropic scaling registration algorithm using bi-directional distance and correntropy

• The scale factor is introduced to deal with the oral cavity registration of multi-source data sets. • Correntropy is introduced into the scaling registration algorithm to suppress noise and outliers interference. • Bi-directional distance is used to ensure the robustness of the proposed algorithm. In orthodontics, a patient is collected a lot of 3D oral cavity data, including oral cavity gypsum and scan data sets. To accurately measure the patient's tooth movement, this paper proposes a robust and precise isotropic scaling registration algorithm using bi-directional distance and correntropy. Firstly, because the oral cavity gypsum data sets have a lot of gypsum tumors and bubbles, which can cause the accuracy of registration results to decrease. Then, we introduce the correntropy into the traditional scaling registration model. Secondly, since unconstrained scaling registration is an ill-posed problem, bi-directional distance is used to enhance the robustness. In this way, a registration model using bi-directional distance and correntropy is established. In order to solve this problem, this paper proposes a new registration algorithm with iterative closest point. Moreover, the convergence of the algorithm is proved theoretically. Finally, the proposed algorithm is tested on the orthodontic database, and our experimental results demonstrate that our algorithm performs robust and high accuracy.

Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.

BackgroundPredictions of cardiovascular disease risks based on health records have long attracted broad research interests. Despite extensive efforts, the prediction accuracy has remained unsatisfactory. This raises the question as to whether the data insufficiency, statistical and machine-learning methods, or intrinsic noise have hindered the performance of previous approaches, and how these issues can be alleviated.ObjectiveBased on a large population of patients with hypertension in Shenzhen, China, we aimed to establish a high-precision coronary heart disease (CHD) prediction model through big data and machine-learningMethodsData from a large cohort of 42,676 patients with hypertension, including 20,156 patients with CHD onset, were investigated from electronic health records (EHRs) 1-3 years prior to CHD onset (for CHD-positive cases) or during a disease-free follow-up period of more than 3 years (for CHD-negative cases). The population was divided evenly into independent training and test datasets. Various machine-learning methods were adopted on the training set to achieve high-accuracy prediction models and the results were compared with traditional statistical methods and well-known risk scales. Comparison analyses were performed to investigate the effects of training sample size, factor sets, and modeling approaches on the prediction performance.ResultsAn ensemble method, XGBoost, achieved high accuracy in predicting 3-year CHD onset for the independent test dataset with an area under the receiver operating characteristic curve (AUC) value of 0.943. Comparison analysis showed that nonlinear models (K-nearest neighbor AUC 0.908, random forest AUC 0.938) outperform linear models (logistic regression AUC 0.865) on the same datasets, and machine-learning methods significantly surpassed traditional risk scales or fixed models (eg, Framingham cardiovascular disease risk models). Further analyses revealed that using time-dependent features obtained from multiple records, including both statistical variables and changing-trend variables, helped to improve the performance compared to using only static features. Subpopulation analysis showed that the impact of feature design had a more significant effect on model accuracy than the population size. Marginal effect analysis showed that both traditional and EHR factors exhibited highly nonlinear characteristics with respect to the risk scores.ConclusionsWe demonstrated that accurate risk prediction of CHD from EHRs is possible given a sufficiently large population of training data. Sophisticated machine-learning methods played an important role in tackling the heterogeneity and nonlinear nature of disease prediction. Moreover, accumulated EHR data over multiple time points provided additional features that were valuable for risk prediction. Our study highlights the importance of accumulating big data from EHRs for accurate disease predictions.

Developing a Risk Management Model for Banking Software Development Projects Based on Fuzzy Inference System

Risk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology (IT) systems in all fields and the high failure rate of IT projects in software development and production, it is essential to effectively manage these projects is essential. Therefore, this study is aimed to design a risk management model that seeks to manage the risk of software development projects based on the key criteria of project time, cost, quality and scope. This is presented after making an extensive review of the literature and asking questions from experts in the field. In this regard, after identifying the risks and defining them based on the dimensions and indicators of software development projects, 22 features were identified to evaluate banking software projects. The data were collected for three consecutive years in the country's largest software development eco-system. According to Rough modelling, the most important variables affecting the cost, time, quality and scope of projects were identified and the amount of risk that a project may have in each of these dimensions was shown. Since traditional scales cannot provide the accurate estimation of project risk assessment under uncertainty, the indexes were fuzzy. Finally, the fuzzy expert system was designed by MATLAB software that showed the total risk of each project. To create a graphical user interface, the MATLAB software GUIDE was used. The system can predict the risks of each project before each project begins and helps project managers be prepared to deal with these risks and consider ways to prevent the project from failing. The results showed that quality and time risks were more important than cost and scope risks and had a greater impact on total project deviation.

O12 Utilizing Sensory Evaluation Methodology in the Development of Plant-based Protein Entrees for the National School Lunch Program (NSLP)

Background The National School Lunch Program (NSLP) provides 30 million meals daily to students across the United States. Offering plant-based protein entrees that students will like and consume during lunchtime is important to increase nutritional intake. Little is known about the acceptability of plant-based entrees in a student population or school lunch setting. Objective To utilize sensory evaluation methodology to assess acceptability, including overall liking (OL) and liking of specific sensory attributes (appearance, flavor, texture) of 3 newly developed plant-based protein entrees to be served as a part of the NSLP. Study Design, Setting, Participants Thirty freshman-level students at a high school in Georgia conducted sensory evaluation for 3 newly developed plant-based protein recipes (bean-based macaroni and cheese, lentil-based sloppy joes, sweet potato chili fries). Recipes were developed to meet NSLP standards to address the recent popularity of plant-based meat alternatives, and to incorporate the taste preferences of student populations. Measurable Outcome/Analysis Sensory evaluation ballots used a traditional 9-point hedonic scale to evaluate OL, specified sensory attributes (1 = dislike extremely, 5 = neither like nor dislike, and 9 = like extremely), and future purchasing behavior. Standard range for recipe acceptability is ≥7. Results The macaroni and cheese recipe had the highest mean OL = 5.70 (appearance = 5.2, flavor = 5.70, texture = 4.8). The sloppy joes had a mean OL = 5.4 (appearance = 3.9, flavor = 5.6, texture = 5.3). The chili sweet potato fries also had a mean OL = 5.4 (appearance = 4.3 flavor = 5.8, texture = 5.8). There was a statistically significant difference (α ≤ 0.05) for liking of appearance between sloppy joes and macaroni and cheese recipe. Conclusion Findings suggest sensory evaluation methodology can be useful in the development of healthy entrees that are responsive to taste preferences of the student population and will be both nutritious and profitable on the school lunch menu. Funding University of Georgia Department of Foods and Nutrition, Jackson County School District.

Measuring Achievement Striving via a Situational Judgment Test: The Value of Additional Context

The study extends personality and situational judgment test (SJT) research by using an SJT to measure achievement striving in a contextualized manner. Employed students responded to the achievement striving SJT, traditional personality scales, and workplace performance measures. The SJT was internally consistent, items loaded on a single factor, and scores converged with other measures of achievement striving. The SJT provided incremental criterion-related validity for the performance criteria beyond less-contextualized achievement striving measures. Findings suggest that achievement-related work scenarios may provide additional criterion-relevant information not captured by measures that are less contextualized.

Agile and Open-Source Hardware

The thirteen articles in this special section focus on agile and open source hardware. These papers cover variety of research topics related to fast, agile, and open hardware design, including methodologies, languages, abstractions, and simulators. As the benefits f traditional technology scaling, like Dennard Scaling and Moore’s Law, slow significantly, computer architecture is poised to enter a golden age of innovation. Domain-specific architectures (DSA) are a promising solution to continue improving computing performance, while maintaining the level of energyand area-efficiency previously found in technology scaling. Unfortunately, traditional methodologies of chip design and hardware development have created significant barriers, requiring extremely high non-recurring engineering costs in tools, labor, IPs, and time, which ultimately hold back broad adoption of DSA.

Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes

Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that adds domain-specific data preprocessing and augmentation to state-of-the-art CNN architectures. Our major focus is to stabilise the prediction performance over samples as a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. To validate the architecture-independent effects of our approach we compare a neural architecture based on dilated convolutions for parallel multi-scale processing (a modified Mixed-Scale Dense Network: MS-D Net) to traditional scaling operations (a modified U-Net). Finally, we show that an ensemble model combines the strengths across different individual methods. Our framework is simple to implement into existing deep learning pipelines for CT analysis. It performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples.

Entrepreneurship education as an arena for career reflection: the shift of students' career preferences after a business planning course

PurposeThe purpose of this paper is to examine the impact of entrepreneurship education (EE) in the form of a business planning course on students' career intentions and preferences. While there is extensive research in which traditional survey scales have been applied to study students' entrepreneurial intentions, this study takes a novel approach by extending the construct of entrepreneurial intention to include preferences for intrapreneurship and team entrepreneurship. Furthermore, the use of conjoint analysis captures students' unconscious decision-making processes when presented with different career opportunity scenarios, thereby overcoming many of the limitations of self-reported survey measures.Design/methodology/approachThe study uses a quasi-experimental design with a novel application of conjoint analysis in EE research. A two-part survey combining a traditional questionnaire with conjoint analysis was distributed to students enrolled in a business planning course at two campuses of a Norwegian university, resulting in 99 matched pre- and post-test responses.FindingsTwo main findings arise from the study. First, there is a significant decrease in entrepreneurial intention among students in the EE course. Second, the conjoint analysis contributes to a better understanding of this decrease by illustrating how students shift their career preferences from entrepreneurship to employment during the EE course. This suggests that EE provides a space for students' career reflections where they can explore, commit to and reconsider entrepreneurship as a career.Research limitations/implicationsA limitation of the study is that it focuses on a small sample of undergraduate students from two campus locations in Norway. Thus, further investigation is still necessary to establish whether the findings are valid in other contexts. The research has implications for higher educational institutions, policymakers and researchers in the field of EE.Practical implicationsThe study contributes with a novel perspective on EE as a trigger for career reflection, a perspective that is important for educators teaching EE courses, as well as for higher education institutions who decide to implement EE in study programmes.Originality/valueBy focusing on the development of students' career preferences through conjoint analysis, the study expands knowledge on the impact of EE on students' careers, while also accentuating the value of the application of conjoint analysis in research on EE.

Developing the First Intensity Prediction Equation Based on the Environmental Scale Intensity: A Case Study from Strong Normal-Faulting Earthquakes in the Italian Apennines

AbstractEarthquakes produce effects on the built and natural environment, the severity of which decays with distance from the epicenter. Empirical relations describing the intensity attenuation with distance are fundamental for seismic hazard assessment and for deriving parameters for preinstrumental events. Seismic intensity is usually assigned based on damage to buildings and infrastructures; this can be challenging for intensity degrees higher than X or when macroseismic fields of multiple events close in time are overlapping. A complementary approach is the study of earthquake environmental effects (EEEs), which are used to assign intensity on the environmental scale intensity (ESI) scale. However, a quantitative comparison between the ESI and traditional scales, and an equation describing the ESI attenuation with distance are still lacking. Here, we analyze 14 historical and instrumental events (time window 1688–2016) in the central and southern Apennines (Italy), comparing ESI and Mercalli–Cancani–Sieberg (MCS) intensities. Our results show that ESI consistently provides higher intensity near the epicenter and the attenuation is steeper than MCS. We derive the first intensity prediction equation for the ESI scale, which computes local intensity as a function of distance and epicentral intensity value. We document that, in the near field, the MCS attenuation for shallow crustal events occurred in the twenty-first century is steeper than previous events, whereas the ESI attenuation shows a consistent behavior through time. This result questions the reliability of current empirical relations for the investigation of future events. We recommend including EEEs in intensity assignments because they can guarantee consistency through time and help in evaluating the spatial and temporal evolution of damage progression during seismic sequences, thus ultimately improving seismic risk assessment.

Reassessment of macroseismic intensities for two earthquakes in Colombia: Tumaco (1979, Mw = 8.1) and Armenia (1999, Mw = 6.1), using the ESI-2007 scale

A reassessment of the macroseismic intensities was performed for the Tumaco (December 12th, 1979, Mw = 8.1) and Armenia (January 25th, 1999, Mw = 6.1) earthquakes, based on the spatial distribution and quantification of dimensions of the surface geological and environmental effects in lists compiled from published documents. The newly constructed lists of 46 effects for the Tumaco earthquake (TE) and 123 effects for the Armenia earthquake (AE) were obtained by literature review and systematic searches of information in authoritative sources. The geological and environmental effects caused by the two earthquakes were then used to estimate seismic intensities and construct new isoseismal maps following the methodology of the ESI-2007 scale. Comparisons were made between the ESI-2007 isoseismal maps and those using data from traditional intensity scales (Modified Mercalli MM-31 and EMS-98) in terms of the percentage overlap between affected areas for each intensity level. For the AE, the newly computed intensities indicate epicentral intensity I0AE = 10 and the ESI-2007 intensities show best agreement with EMS-98 values, with a maximum of 54.2% overlap for intensities in the range 7–8. The new isoseismal maps for the TE indicate maximum intensity IMaxTE ~ I0TE = 11 and best agreement with MM-31 values, with a maximum of 48.8% overlap for intensities in the range 7–8. It is concluded that ESI-2007 intensities exhibit close agreement in most cases with MM-31 and EMS-98 values and provide new details for maximum intensities and the extent of affected areas which can be used in future plans of land use for earthquake-prone areas in Colombia.

Development and Psychometric Evaluation of the PROMIS Pediatric Pain Intensity Measure in Children and Adolescents with Chronic Pain

The goal of the current study was to enhance the measurement of the pediatric chronic pain experience through a methodologically rigorous approach. This paper outlines the development and initial validation of a pain intensity measure for pediatric patients with chronic pain using Patient-Reported Outcomes Measurement Information System methodology. Measure development incorporated feedback from children with painful conditions. Based on input from pediatric participants and content experts, 4 candidate items assessing pain intensity were included for large scale testing. Children completed self-report items pertaining to their pain experience that were developed as part of a larger pool of new candidate Patient-Reported Outcomes Measurement Information System pediatric pain domain items as well as measures of pain interference, depressive symptoms, fatigue, pain behavior, pain intensity, and pain catastrophizing. The final sample for the large scale testing included N = 442 pediatric patients between the ages 8 to 18 years (Mean age = 13.54, Standard Deviation = 2.78; 71.27% female) experiencing chronic pain. Psychometric analysis resulted in a final measure that included 3 items with evidence of reliability (Cronbach alpha = .82) and convergent validity. The Likert format of the response options may be preferable to the traditional numeric rating scale for use in pediatric populations who experience chronic pain based on patients’ feedback, which was directly utilized in designing the scale. Further, the inclusion of fewer and clinically meaningful response options should reduce ambiguity for young respondents. PerspectiveWe have developed and evaluated a clinically sensitive and psychometrically precise 3-item pain intensity measure with Likert-type responses for self-report use among children and adolescents ages 8 to 18 years with chronic pain. Development of the item content and response options included input from children and adolescents with chronic pain. The development of pain intensity items with pediatric appropriate language, and labeled, fewer response options to yield maximal clinically meaningful information improves the precision of pain intensity measurement in children.

Neighbourhood collective efficacy and protective effects on child maltreatment: A systematic literature review

Research within the community-based child protection approach has used the neighbourhood collective efficacy theory of social disorganisation to focus on investigating the social conditions and processes that facilitate residents' ability to intervene or protect children from parental maltreatment. However, much of the research into the protective effects of neighbourhood collective efficacy on child maltreatment has yielded mixed results. In a review of empirical studies published between 2008 and 2019, we investigated the sources of these mixed findings and the pathways through which neighbourhood collective efficacy could protect children from parental maltreatment. Following the PRISMA guidelines for systematic literature reviews yielded 21 empirical research articles on the subject that were critically examined in line with the theoretical underpinning and research questions. Evidence suggests both direct and sequential pathways in which increased social cohesion and informal social control (ISC) protect against parents' maltreatment behaviours. Higher levels of neighbourhood social cohesion were found to be a potential primary preventive strategy against risk factors for maltreatment. The use of ISC measures from the traditional collective efficacy scale account for the mixed findings and limited research on the direct and indirect forms of ISC. Moreover, the transactional processes posited by collective efficacy theory that link neighbourhood social cohesion to ISC have yet to be examined and confirmed with respect to child maltreatment. Studies addressing these theoretical and methodological gaps are encouraged, in particular, studies examining ISC dimensions using item measures of specific residents' actions within child maltreatment behaviours. The results provide implications for community-based child protection practice, in terms of promoting cultural norms and values that foster social cohesion and facilitate ISC interventions within neighbourhoods.

Expressions of IFN-γ and IL-4 before and after Treatment of Lupus Nephritis with Traditional Chinese Medicine Combined with Cyclophosphamide and Their Values for Efficacy Prediction and Evaluation

To explore IFN-γ (interferon-γ) and IL-4 (interleukin-4) expressions before and after the treatment of LN (lupus nephritis) and their values for efficacy prediction and evaluation. Altogether 107 patients with LN treated in the First Hospital of Qiqihaer City, Qiqihar, China from March 2017 to September 2018 were enrolled. Sixty-two patients were treated with cyclophosphamide and prednisolone (control group), while another 45 patients were treated with Qing Shen Fang based on the control group (observation group). Their clinical efficacy and changes in immune indices after treatment were observed. Compared with those in the control group, clinical efficacy, IFN-γ, IL-4, hemoglobin, complements C3 and C4, ESR (erythrocyte sedimentation rate), serum IgG, SLEDAI (Systemic Lupus Erythematosus Disease Activity Index) score, and TCMSSS (Traditional Chinese Medicine Syndrome Score Scale) score were significantly improved after treatment in the study group. Based on the observation, IFN-γ and IL-4 could be used as potential indicators for evaluating clinical efficacy. The combination of cyclophosphamide, prednisolone, and Qing Shen Fang improves conditions of patients with LN and significantly reduces their IFN-γ and IL-4 levels in serum. IFN-γ and IL-4 can be used as potential indicators for the efficacy prediction and evaluation of the disease.