Lasater clinical judgment rubric in nursing education: a Turkish validity and reliability study

Abstract Introduction Gaining clinical judgment competence among student nurses is a significant outcome of nursing education. In this education process, an assessment tool based on observable behaviors is needed for evaluating students’ clinical judgment skills. Objective This study aimed to evaluate the validity and reliability of the Turkish version of the Lasater Clinical Judgment Rubric, which assesses student nurses’ stages of clinical judgment competency in simulation-based education. Method This study was conducted using a cross-sectional methodological design between April and August 2024. For the cultural adaptation and psychometric evaluation of the rubric, simulation videos of 3rd and 4th-year nursing students from a foundation university were used. The language validity of the rubric was performed according to the 10-step translation and cultural adaptation guide of the International Society for Pharmacoeconomics and Outcomes Research; expert opinions were obtained for content validity. The final version of the rubric comprises four stages of the Tanner Model of Clinical Judgment (noticing, interpreting, responding, and reflecting) and 11 dimensions. Four levels of achievement describe the development of each of the 11 dimensions. Sixty-four scenario records of the simulation-based education of 4th -year students in the nursing undergraduate program of a foundation university were examined, and the students were evaluated by two independent observers using the Turkish version of the rubric. The construct validity of the scale was examined using confirmatory factor analysis, and reliability was measured using Cronbach’s alpha coefficient. Interobserver agreement was analyzed using the kappa and intraclass correlation coefficients. Moreover, the discrimination of the rubric was evaluated using an independent samples t-test between the lower and upper 27% groups. Results Confirmatory factor analysis revealed that the scale formed a four-factor structure of noticing, interpreting, responding, and reflecting in accordance with the original rubric. Furthermore, confirmatory factor analysis showed that the four-factor structure model had an acceptable and generally good fit and was statistically significant and valid. The fit indices for the model were calculated as χ2/df = 1.70, root mean square error of approximation = 0.06, comparative fit index = 0.94, and root mean square residual = 0.03. Kappa values ranged between 0.72 and 0.92, indicating a significant fit for all the items. The intraclass correlation coefficient values ranged between 0.70 and 0.90 and were statistically significant for all the items. The Cronbach’s alpha value of the rubric was 0.91 and ranged between 0.80 and 0.84 in its subdimensions. Conclusion Therefore, the Turkish version of the rubric is a valid and reliable tool for evaluating student nurses’ clinical judgment competency in simulation-based training

CFD analysis of heat transfer enhancement in impinging jet array by varying number of jets and spacing

Abstract Using the RANS approach with the standard k-ω turbulence model, this study offers a novel investigation into the dynamic and thermal properties of turbulent impinging jet arrays. Our study examines the combined effect of the number of jets (N) and the jet–jet spacing (S) on flow mechanisms and heat transfer performance, which is unique compared to previous research that frequently focuses on the individual effects of parameters. Through the investigation of the turbulent kinetic energy, friction coefficient, velocity contours, streamlines, pressure contours, and local and mean Nusselt numbers, we provide important information about how these parameters impact flow dynamics. Local heat transfer in the central and lateral zones is greatly improved by increasing the number of jets (N) and the jet–jet spacing (S), according to our findings. When the jet–jet spacing (S) is increased from 1 to 4, the maximum value of the Nusselt number along the central zone improves by 21.2%. Furthermore, the best improvement in the maximum Nusselt number (24.5%) along the lateral zone is obtained by increasing the number of jets (N) from 5 to 11 for the lower value of jet–jet spacing S = 1. It has also been noted that lower jet-plate distance (H), lower jet–jet spacing (S), and a higher number of jets (N) result in better average heat transfer. To predict the average Nusselt number based on three parameters (N, S, and H), we establish a critical correlation, which provides a useful tool for optimizing impinging jet configurations in a variety of engineering applications. The diversification of the parameters studied and the thorough analysis in this study add important new results to the field by demonstrating the significant effects of the number of jets, jet–jet spacing, and jet-plate distance on the thermal and dynamic behavior of impinging jet arrays

Moderate stability of risk and ambiguity attitudes across quantitative and qualitative decisions

Abstract Uncertainty lies at the heart of everyday choices, affecting both decisions about precise quantities and those with less tangible, more qualitative, outcomes. Previous literature on decisions under uncertainty focused on alternatives with quantifiable outcomes, for example monetary lotteries. In such scenarios, decision-makers make decisions based on success chance, outcome magnitude, and individual preferences for uncertainty. It is not clear, however, how individuals construct subjective values when outcomes are not directly quantifiable. To explore how decision-makers choose between non-quantifiable uncertain outcomes, we focus here on medical decisions with qualitative outcomes. Specifically, we ask whether decision-makers exhibit similar attitudes towards uncertainty, focusing on ambiguity, across domains with quantitative and qualitative outcomes. We designed an online decision-making task where participants made binary choices between alternatives offering either guaranteed low outcomes or potentially better outcomes that were associated with some uncertainty. Outcomes were either hypothetical monetary gains of varying magnitudes or levels of improvement in a hypothetical medical condition. We recruited 429 online participants and repeated the survey in two waves, which allowed us to compare the between-domain attitude consistency with within-domain consistency over time. We found that uncertainty attitudes were moderately correlated across domains and time. We discuss the implications and applicability of our paradigm to broader contexts with non-quantifiable outcomes

Cardiac injury caused by iron overload in thalassemia

Cardiac iron overload affects approximately 25% of patients with β-thalassemia major, which is associated with increased morbidity and mortality. Two mechanisms are responsible for iron overload in β-thalassemia: increased iron absorption due to ineffective erythropoiesis and blood transfusions. This review examines the mechanisms of myocardial injury caused by cardiac iron overload and role of various clinical examination techniques in assessing cardiac iron burden and functional impairment. Early identification and intervention for cardiac injury and iron overload in β-thalassemia have the potential to prevent and reverse or delay its progression in the early stages, playing a crucial role in its prognosis

The utility of muscle magnetic resonance imaging in idiopathic inflammatory myopathies: a scoping review

Idiopathic inflammatory myopathies (IIMs) are muscle disorders characterized by proximal weakness of the skeletal muscles, inflammation in muscle, and autoimmunity. The classic subgroups in IIMs include dermatomyositis, inclusion body myositis, immune-mediated necrotizing myopathy, and polymyositis (PM). PM is increasingly recognized as a rare subtype and often included in overlap myositis, the antisynthetase syndrome when no rash is present, or misdiagnosed inclusion body myositis. Magnetic resonance imaging (MRI) has played an increasingly important role in IIM diagnosis and assessment. Although conventional MRI provides qualitative information that is helpful for diagnosis, its application for the quantitative assessment of disease activity is challenging. Therefore, advanced quantitative MRI techniques have been implemented in the past 10 years to highlight potential new applications of disease monitoring in IIM. The aim of this review is to examine the role of quantitative MRI techniques in evaluating the key imaging features of IIM, mainly muscle edema and muscle damage (fatty replacement and/or muscle atrophy)

Genome-wide identification of the bHLH gene family in Scutellaria baicalensis and their relationship with baicalin biosynthesis under drought stress

The bHLH gene family plays a critical role in regulating internal responses in plants. Although the pharmacological properties of Scutellaria baicalensis have been extensively studied, its bHLH gene family remains poorly investigated. In this study, 142 SbbHLH genes were identified using the complete genome data of S. baicalensis. Phylogenetic and conserved motif analyses were performed. Gene duplication events were analyzed, and cis-element analysis was conducted to explore regulatory factors. The expression patterns of these genes in different tissues and under drought stress were investigated using transcriptome data and qRT-PCR analysis. Phylogenetic and conserved motif analyses revealed that the gene structures within each SbbHLH clade are relatively conserved. Gene duplication analysis identified 29 duplication events in the SbbHLH gene family, most of which involved gene pairs under purifying selection. Cis-element analysis revealed that these genes are regulated by various environmental and hormonal factors. Transcriptomic data and qRT-PCR results demonstrated tissue-specific expression patterns for the 142 SbbHLH genes. Additionally, bHLH genes potentially involved in baicalin biosynthesis were identified under drought stress. The findings suggest that under drought stress, SbbHLH74, SbbHLH98, and SbbHLH142 are regulated by a network centered on SbbHLH53, which enhances baicalin biosynthesis. In conclusion, this study provides a comprehensive analysis of the bHLH gene family in S. baicalensis and identifies 4 potential SbbHLH genes involved in regulating baicalin biosynthesis under drought stress

Evaluation of the efficacy of a novel lumbar exoskeleton with multiple interventions for patients with lumbar disc herniation: a multicenter randomized controlled trial of non-inferiority

BackgroundLumbar disc herniation (LDH) occurs when the nucleus pulposus or annulus fibrosus protrudes into the intervertebral space, potentially compressing nerve roots and causing symptoms such as sciatica, restricted mobility, and lower extremity weakness. The development of portable lumbar exoskeleton devices offers a new approach, combining traction, range of motion (ROM) exercises, and resistance training in a single system, potentially reducing treatment complexity and enhancing LDH patient outcomes.ObjectiveThis study aims to evaluate the efficacy and safety of a novel lumbar exoskeleton device compared to traditional traction methods combined with rehabilitation therapy for patients with LDH.MethodsA multicenter, non-inferiority randomized controlled trial was conducted with 118 participants diagnosed with LDH. Participants were randomly assigned to the Exoskeleton Group or the Traction Group. The Exoskeleton Group used the novel device for traction, ROM, and resistance training, while the Traction Group underwent traditional traction and rehabilitation therapy. Outcomes included efficacy rate after 10 treatments, Visual Analogue Scale (VAS), Oswestry Disability Index (ODI), and lumbar ROM—were assessed at 3, 6, and 10 treatments.ResultsA total of 118 eligible participants were recruited. After 10 treatments, both groups showed significant improvements in VAS scores, ODI, and lumbar ROM compared to baseline (P < 0.001). However, there was no significant difference in the overall efficacy rate between the two groups (P = 0.748).ConclusionThe novel lumbar exoskeleton device demonstrates comparable efficacy and safety to traditional traction therapy combined with rehabilitation, offering a promising alternative for the conservative treatment of LDH

Study on influencing factors of age-adjusted Charlson comorbidity index in patients with Alzheimer's disease based on machine learning model

BackgroundAlzheimer's disease (AD) is a widespread neurodegenerative disease, often accompanied by multiple comorbidities, significantly increasing the risk of death for patients. The age adjusted Charlson Comorbidity Index (aCCI) is an important clinical tool for measuring the burden of comorbidities in patients, closely related to mortality and prognosis. This study aims to use the MIMIC-V database and various regression and machine learning models to screen and validate features closely related to aCCI, providing a theoretical basis for personalized management of AD patients.MethodsThe research data is sourced from the MIMIC-V database, which contains detailed clinical information of AD patients. Multiple logistic regression, LASSO regression, random forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) models were used to screen for feature factors significantly correlated with aCCI. By comparing model performance, evaluating the classification ability and prediction accuracy of each method, and ultimately selecting the best model to construct a regression model and a nomogram. The model performance is evaluated through classification accuracy, net benefit, and robustness. The feature selection results were validated by regression analysis.ResultsMultiple models have performed well in classifying aCCI patients, among which the model constructed using LASSO regression screening feature factors has the best performance, with the highest classification accuracy and net benefit. LASSO regression identified the following 11 features closely related to aCCI: age, respiratory rate, base excess, glucose, red blood cell distribution width (RDW), alkaline phosphatase (ALP), whole blood potassium, hematocrit (HCT), phosphate, creatinine, and mean corpuscular hemoglobin (MCH). The column chart constructed based on these feature factors enables intuitive prediction of patients with high aCCI probability, providing a convenient clinical tool.ConclusionThe results of this study indicate that the features screened by LASSO regression have the best predictive performance and can significantly improve the predictive ability of aCCI related comorbidities in AD patients. The column chart constructed based on this feature factor provides theoretical guidance for personalized management and precise treatment of AD patients

The mutation atlas of giant kelp (Macrocystis pyrifera): a mutation database resource for natural knockouts

Giant kelp (Macrocystis pyrifera) is a paramount species of immense ecological and economic importance. It forms dense underwater forests, providing crucial habitat and serving as a foundation species for diverse marine ecosystems. Understanding the genetics of giant kelp is essential for conservation and sustainable farming, safeguarding these valuable ecosystems and their benefits. By analyzing mutations based on their impact, we can gain insights into the potential functional consequences and implications for the organism, helping to identify critical genes or regions that may play a significant role in adaptation, development, and environmental response. To achieve this, we annotated the effects and impact of spontaneous mutations in 559 giant kelp individuals from four different populations. We found over 15.9 million mutations in genes of giant kelp, and classified them into modifier, low, moderate, and high impact depending on their predicted effects. The creation of this mutation effect database, attached to the seedbank of these individuals, offers several applications, including enhancing breeding programs, aiding genetic engineering with naturally occurring mutations, and developing strategies to mitigate the impact of environmental changes

Synergistic Hierarchical Bi-Level Optimization of Demand Response Programs and Smart Inverter Volt/Var Control for Maximizing Hosting Capacity of Power Systems With Renewable-Based Distributed Generators

Renewable-based distributed generation (DG) offers economic and environmental benefits, but integrating it into the distribution network (DN) poses technical challenges. Hosting capacity (HC) defines the maximum amount of DG that can be safely integrated into the DN without violating technical constraints, with various methods available to enhance the DN and increase HC. This article proposes an optimization-based demand response (DR) program using dynamic pricing to enhance the HC. A hierarchical bi-level optimization framework is developed for the DR program, where the upper level optimizes the hosting capacity depending on the optimal load profiles optimized in the lower level. Additionally, DR is combined with smart inverters’ (SIs) optimal Volt/VAR control to further improve the HC. The pelican optimization algorithm (POA) is employed to solve the optimization problem and is compared with four other optimization techniques, demonstrating its good performance. The proposed planning scheme is implemented on the IEEE 33-bus test system and a real DN consisting of 59 buses in Cairo, Egypt. The results show that applying DR alone can increase the average maximum HC value by 29.4%, while combining DR with optimal Volt/VAR control can achieve a 39.6% increase