Search Results (269)

Background/Objectives: Laboratory tests conducted in community settings are occasionally repeated within hours of presentation to pediatric emergency departments (PEDs). Reducing unnecessary repetitions can ease child discomfort and alleviate the healthcare burden without compromising the diagnostic process or quality of care. The aim of this study was to develop a decision tree (DT) model to guide physicians in minimizing unnecessary repeat blood tests in PEDs. The minimal decision tree (MDT) algorithm was selected for its interpretability and capacity to generate optimally pruned classification trees. Methods: Children aged 3 months to 18 years with community-based complete blood count (CBC), electrolyte (ELE), and C-reactive protein (CRP) measurements obtained between 2016 and 2023 were included. Repeat tests performed in the pediatric emergency department within 12 h were evaluated by comparing paired measurements, with tests considered justified when values transitioned from normal to abnormal ranges or changed by ≥20%. Additionally, sensitivity analyses were conducted for absolute change thresholds of 10% and 30% and for repeat intervals of 6, 18, and 24 h. Results: Among 7813 children visits in this study, 6044, 1941, and 2771 underwent repeated CBC, ELE, and CRP tests, respectively. The mean ages of patients undergoing CRP, ELE, and CBC testing were 6.33 ± 5.38, 7.91 ± 5.71, and 5.08 ± 5.28 years, respectively. The majority were of middle socio-economic class, with 66.61–71.24% living in urban areas. Pain was the predominant presented complaint (83.69–85.99%), and in most cases (83.69–85.99%), the examination was conducted by a pediatrician. The DT model was developed and evaluated on training and validation cohorts, and it demonstrated high accuracy in predicting the need for repeat CBC and ELE tests but not CRP. Performance of the DT model significantly exceeded that of the logistic regression model. Conclusions: The data-driven guide derived from the DT model provides clinicians with a practical, interpretable tool to minimize unnecessary repeat laboratory testing, thereby enhancing patient care and optimizing healthcare resource utilization. Full article

Introduction: Sepsis has a high mortality rate, especially in low-income countries. Improving outcomes depends on the early recognition of patients at risk of death. Therefore, rapid and applicable prediction scores are needed in emergency triage. Objective: This study assessed the effectiveness of the qSOFA, NEWS, and IEWS scales in predicting in-hospital mortality during emergency triage. Additionally, we analyzed the efficacy of the IEWS_L, which integrates the IEWS with arterial lactate levels measured upon admission to the emergency department. Method: This retrospective study included patients who consulted the emergency department with suspected sepsis, where various scoring systems were evaluated for their effectiveness. We evaluated the diagnostic capacity of the tests by measuring the specificity, sensitivity, positive and negative predictive values, as well as the areas under the curve (AUC) of each score to predict mortality. Results: The study included 383 patients who had visited the emergency department. The overall mortality rate was 20.6%, and the mortality rate, precisely due to septic shock, was 35.2%. The AUC values for predicting in-hospital deaths due to sepsis were as follows: qSOFA: 0.68 (95% CI: 0.62–0.74); NEWS: 0.71 (95% CI: 0.64–0.77); IEWS: 0.74 (95% CI: 0.68–0.80); IEWS_L: 0.81 (95% CI: 0.76–0.86). Conclusions: In the emergency department, the IEWS scale demonstrated the best ability to accurately predict in-hospital mortality from sepsis when compared to the qSOFA and NEWS scale. Additionally, incorporating the serum lactate level into the IEWS scale significantly enhances its capacity to predict mortality. Full article

Background/Objectives: This study investigates the evolution of hospital capacity and utilization in California between 2003 and 2023, focusing on emergency departments (EDs) and trauma centers (TCs). We seek to document structural changes in the healthcare delivery system with respect to hospital-based emergency and trauma services. Methods: This analysis examines changes in population demographics, hospital resources, and patient utilization patterns across facility types. Given the significant increase in the proportion of the population aged 65+ and the documented higher use of emergency and trauma services by this population, we expected to observe an expansion in ED and trauma service capacity and utilization. Results: Utilizing a comprehensive dataset of California general acute care hospitals over this 20+ year period, our descriptive analysis reveals major shifts in the healthcare delivery system, notably the increased prominence of hospitals with EDs, particularly those designated as trauma centers. Findings indicate that, while the overall number of hospitals and licensed beds has slightly decreased, facilities with EDs, especially trauma centers, have increased their capacity and manage a greater proportion of inpatient admissions and ED visits. Conclusions: The increase in ED visits and inpatient admissions at trauma centers, contrasted with decreases in both capacity and utilization at non-trauma hospitals, indicates a significant restructuring of the health delivery system with significant implications for healthcare policy, financing, operations, and affordability. The high and increasing percentage of inpatient admissions originating from hospital EDs and from hospitals with trauma centers suggests a need for policies that foster integration between ED and inpatient care and the broader healthcare delivery system, while at the same time managing the increase in prices and costs associated with growing emergency services utilization. Further research is needed to explore the implications of these trends, particularly concerning their impact on the affordability of healthcare in the US. Full article

Background/Objectives: Patients recovering from COVID-19 often experience persistent respiratory symptoms, necessitating pulmonary function monitoring. While clinical spirometry is the gold standard, home spirometry offers a remote alternative. This study evaluated the validity of an ultrasonic home-based spirometer for monitoring lung function in post-COVID-19 pneumonia patients over 12 weeks. Methods: This prospective study included 30 post-COVID pneumonia patients who underwent clinical spirometry at weeks 4, 8 and 12. Participants performed weekly home spirometry using the SpiroHome Personal^® device. Agreement between home and clinical spirometry was assessed using a Bland–Altman analysis, intraclass correlation coefficients (ICCs), and Pearson correlation coefficients. Pulmonary function changes over time were analyzed using repeated measures ANOVA. Results: Home spirometry showed strong agreement with clinical spirometry for forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1), with ICC values exceeding 0.92. The Bland–Altman analysis demonstrated minimal bias, though limits of agreement exceeded the clinically accepted threshold of ±150 mL. FEV1/FVC ratios showed greater variability. Pulmonary function improved significantly over 12 weeks for both methods (p < 0.002). Patient adherence to home spirometry remained high, with a median of 18.50 sessions [IQR: 15.00–26.00] and an overall compliance rate of 98.33% ± 9.13%. Conclusions: Home spirometry provides reliable pulmonary function measurements, particularly for FVC and FEV1, supporting its role as a remote monitoring tool. Despite minor variability in FEV1/FVC, home spirometry enables frequent assessment of lung function recovery, potentially reducing hospital visits and improving patient management. Full article

Studies on the gain or loss of nectar during the course of evolution in Dendrobium Sw. (Orchidaceae) are able to provide important information concerning the reproductive biology of this enormous orchid genus and highlight reproductive barriers—in particular, changes to compatibility. By employing a literature search on the compatibility system of Dendrobium, supplemented by new experimental data of 13 taxa investigated by means of microscopy, histochemistry, and phylogenetic analysis, we aimed to ascertain whether there is, in this genus, a relationship between self-compatibility (SC) and the presence of nectar. Nectariferous plant species are thought to be visited more frequently by pollinators, resulting in geitonogamy or selfing; therefore, the presence of nectar in some Dendrobium species may promote self-incompatibility (SI), whereas a lack of nectar may increase cross-pollination. Our investigations confirmed that the capacity for nectar secretion was gained and lost several times in this genus, and that similarly organized nectar spurs were present in all species investigated, regardless of their ability to produce nectar. SI, SC, and the presence or absence of nectar have all evolved independently, but, of the 42 taxa investigated whose status both relating to nectar presence and compatibility was known, nectar was more frequent in self-incompatible taxa. Full article

Inland waterway transportation is critical for the movement of hazardous liquid cargoes. To prevent contamination when transporting different types of liquids, certain shipments necessitate tank cleaning at designated stations between tasks. This process often requires detours, which can decrease operational efficiency. This study addresses the Tank Cleaning Station Location and Cleaning Task Assignment (TCSL-CTA) problem, with the objective of minimizing total system costs, including the construction and operational costs of tank cleaning stations, as well as the detour costs incurred by ships visiting these stations. We formulate the problem as a mixed-integer programming (MIP) model and prove that it can be reformulated into a partially relaxed MIP model, preserving optimality while enhancing computational efficiency. We further analyze key mathematical properties, showing that the assignment constraint matrix is totally unimodular, enabling efficient relaxation, and that the objective function exhibits submodularity, reflecting diminishing returns in facility investment. A case study on the Yangtze River confirms the model’s effectiveness, where the optimized plan resulted in detour costs accounting for only 5.2% of the total CNY 4.23 billion system cost and achieved an 89.1% average station utilization. Managerial insights reveal that early construction and balanced capacity allocation significantly reduce detour costs. This study provides a practical framework for long-term tank cleaning infrastructure planning, contributing to cost-effective and sustainable inland waterway logistics. Full article

Artificial Intelligence (AI) is rapidly transforming orthodontic care by providing personalized treatment plans that enhance precision and efficiency. This narrative review explores the current applications of AI in orthodontics, particularly its role in predicting tooth movement, fabricating custom aligners, optimizing treatment times, and offering real-time patient monitoring. AI’s ability to analyze large datasets of dental records, X-rays, and 3D scans allows for highly individualized treatment plans, improving both clinical outcomes and patient satisfaction. AI-driven aligners and braces are designed to apply optimal forces to teeth, reducing treatment time and discomfort. Additionally, AI-powered remote monitoring tools enable patients to check their progress from home, decreasing the need for in-person visits and making orthodontic care more accessible. The review also highlights future prospects, such as the integration of AI with robotics for performing orthodontic procedures, predictive orthodontics for early intervention, and the use of 3D printing technologies to fabricate orthodontic devices in real-time. While AI offers tremendous potential, challenges remain in areas such as data privacy, algorithmic bias, and the cost of adopting AI technologies. However, as AI continues to evolve, its capacity to revolutionize orthodontic care will likely lead to more streamlined, patient-centered, and effective treatments. This review underscores the transformative role of AI in modern orthodontics and its promising future in advancing dental care. Full article

In recent years, routing problems have attracted significant attention in the fields of operations research and computer science due to their fundamental importance in logistics and transportation. However, most existing learning-based methods employ simplistic context embeddings to represent the routing environment, which constrains their capacity to capture real-time visitation dynamics. To address this limitation, we propose a deep reinforcement learning-based decision-making framework (DRL-DM) built upon an encoder–decoder architecture. The encoder incorporates a batch normalization fronting mechanism and a gate-like threshold block to enhance the quality of node embeddings and improve convergence speed. The decoder constructs a dynamic-aware context embedding that integrates relational information among visited and unvisited nodes, along with the start and terminal locations, thereby enabling effective tracking of real-time state transitions and graph structure variations. Furthermore, the proposed approach exploits the intrinsic symmetry and circularity of routing solutions and adopts an actor–critic training paradigm with multiple parallel trajectories to improve exploration of the solution space. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate that DRL-DM consistently outperforms heuristic and learning-based baselines, achieving up to an 8.75% reduction in tour length. Moreover, the proposed method exhibits strong generalization capabilities, effectively scaling to larger problem instances and diverse node distributions, thereby highlighting its potential for solving complex, real-life routing tasks. Full article

Background: Physical, emotional, psychological, and social factors influence the high level of burden of a caregiver providing care for a patient at home after a stroke. The purpose of this study was to identify and evaluate factors influencing the high level of burden on the caregiver providing care for a post-stroke patient, including factors on the part of the patient and caregiver. Methods: This cross-sectional study was conducted at the Neurological Rehabilitation Department of the Hospital Beskid Treatment and Rehabilitation Complex in Jaworze, Poland, and the Neurological Rehabilitation Department of the Railway Hospital in Wilkowice-Bystra. The study participants comprised post-stroke patients and their family caregivers (during visits to hospital), 110 pairs. The measures for caregivers were such as the following: Beck Depression Inventory, the Polish adaptation of the Perceived Stress Scale, the Polish adaptation of the Mini-COPE questionnaire to measure stress coping strategies, and the WHO Quality of Life Brief Version. The measures for patients were such as the following: the modified Rankin Scale and Abbreviated Mental Test Score to assess functional capacity for simple Activities of Daily Living (ADL). All statistical calculations were performed using the R statistical package version 4.4.2. Results: A high caregiver burden was found in 30 people (27.3%). Logistic regression analysis proved that low quality of life, stress, caregiver-triggered strategies (discharge and cessation of activities), caregiver frustration, psychological burden, financial situation, longer time spent on patient care, functional status (ADL) on the part of the patient, judgment of significant degree of disability judgment, and age of the patient are determinants affecting high caregiver burden levels. Conclusions: Almost 1/3 of caregivers experienced a high burden when taking care of a person after stroke. Analyzing the Gini index, from the model’s point of view, quality of life is the most important characteristics, and caregiver frustration is the least important, which influences the high level of caregiver burden. Full article

To enhance the efficiency and accuracy of detecting insulation faults such as discharge carbon traces in large oil-immersed transformers, this study employs an inspection micro-robot to replace manual inspection for image acquisition and fault identification. While the micro-robot exhibits compactness and agility, its limited battery capacity necessitates the critical optimization of its 3D inspection path within the transformer. To address this challenge, we propose a hybrid algorithmic framework. First, the task of visiting inspection points is formulated as a Constrained Traveling Salesman Problem (CTSP) and solved using the Ant Colony Optimization (ACO) algorithm to generate an initial sequence of inspection nodes. Once the optimal node sequence is determined, detailed path planning between adjacent points is executed through a synergistic combination of the A algorithm*, Rapidly exploring Random Tree (RRT), and Particle Swarm Optimization (PSO). This integrated strategy ensures robust circumvention of complex 3D obstacles while maintaining path efficiency. Simulation results demonstrate that the hybrid algorithm achieves a 52.6% reduction in path length compared to the unoptimized A* algorithm, with the A*-ACO combination exhibiting exceptional stability. Additionally, post-processing via B-spline interpolation yields smooth trajectories, limiting path curvature and torsion to <0.033 and <0.026, respectively. These advancements not only enhance planning efficiency but also provide substantial practical value and robust theoretical support for advancing key technologies in micro-robot inspection systems for oil-immersed transformer maintenance. Full article

Micro- and small-sized enterprises (MSEs) play a key role in developing emerging countries’ economies. However, concerns remain about their resilience and continuity, especially during periods of conflict and crisis. To address this gap, this research explores key factors that enhance adaptive capacity (AC) and entrepreneurial resilience (ER) amongst MSEs. Data were collected from 301 micro- and small-sized entrepreneurs operating different business activities using an online questionnaire and on-site visits. Partial least squares–structural equation modeling was employed to analyze the data. Findings revealed that crisis management preparedness (CMP) and customer-centric adaptation (CCA) positively and significantly influence AC, which, in turn, positively affects ER. In addition, CMP and CCA directly influence ER. Moreover, AC partially mediates the relationship between CMP, CCA, and ER. This study offers significant practical and theoretical implications for policymakers in making strategic action plans. Full article

Background: Fitness is relevant for firefighter occupational performance, but its influence on exertion and cognition during occupational tasks remains unclear. We aim to determine fitness’s influence on performance, exertion, and cognition during simulated firefighter occupational tasks. Methods: Two baseline fitness assessments occurred to measure aerobic fitness, strength, power, and body composition in 33 non-firefighter participants (females/males: 15/18; 25.18 ± 4.06 years; 174.01 ± 9.77 cm; 75.94 ± 13.18 kg). A third visit involved participants completing an occupational task assessment (OTA; four rounds of deadlifts, sandbag carries, and a cognitive assessment at 35 °C/50% humidity) to the best of their ability. Multiple linear regression models, including strength and power, aerobic capacity, and body composition, were used to predict OTA performance and exertion. Our outcomes included time to complete, exertion, and cognitive performance during the OTA. Results: The model predicted OTA time, RPE, skin temperature, and blood lactate (ps < 0.02), but not core temperature (p > 0.24). The model did not predict cognitive errors (ps > 0.06). Conclusions: These metrics of fitness provide meaningful predictive insight into firefighters’ occupational readiness during simulated occupational tasks. Full article

Functional somatic symptoms (FSSs) in children—such as headaches, stomachaches, and muscle pain without clear medical explanations—pose a significant clinical challenge, often leading to repeated healthcare visits and impairments in daily functioning. While the role of parental psychological factors in shaping children’s FSSs has been suggested, empirical evidence remains limited and fragmented. This study addresses this gap by systematically examining the associations between parents’ reflective functioning, emotion regulation, alexithymia, and physical and mental health, and the frequency and severity of children’s FSSs. A total of 339 parents of children aged 6–12 completed surveys assessing their capacity to understand mental states, regulate emotions, and identify or describe feelings, as well as their self-reported physical and mental health. They also indicated whether their child experienced FSSs (e.g., headaches, stomachaches) more than once per week. Results revealed that parents of children with FSSs reported significantly lower levels of reflective functioning (lower certainty, higher uncertainty), higher alexithymic traits, and greater emotion regulation difficulties, alongside poorer physical and mental health indices. Logistic regression analyses demonstrated that emotion regulation difficulties and poorer mental health significantly increased the likelihood of a child exhibiting FSSs, while lower reflective functioning also emerged as a significant predictor. Furthermore, multiple linear regression indicated that emotion regulation challenges and poor mental health predicted greater severity of FSSs. These findings offer novel insights into how parents’ psychological and health characteristics can shape children’s somatic symptom expression, highlighting the need for family-focused interventions. By identifying and addressing parental emotional and cognitive difficulties, clinicians may be able to mitigate the intergenerational transmission of maladaptive stress responses, ultimately reducing the burden of FSSs in children. Full article

The ability of parents to maintain visitation with their child after a care order is a complex aspect of child welfare. While visitation is widely recognized as essential for preserving family bonds and supporting potential reunification, less attention has been given to how broader life circumstances influence a parent’s ability to engage in visitation. This study explores how parents describe their life situation after a care order and examines how different contextual factors may relate to their visitation capacity. The study employs thematic analysis based on interviews with 31 parents whose children were placed in public care. The findings reveal that parents face multiple barriers that affect their ability to sustain meaningful contact with their child, including emotional and psychological strain, social isolation and stigma, trust and cooperation challenges, and shifting parent–child relationship dynamics. Many parents described profound distress following the care order, marked by anxiety, grief, and loss of parental identity. Additionally, strained relationships with child welfare professionals, inconsistent expectations, and systemic barriers further complicate their efforts to remain engaged. These findings highlight that visitation capacity is not merely a matter of legal access but is shaped by broader life circumstances, emotional resilience, and institutional support structures. Full article

The Cantabrian region, located in north Spain, is home to many caves with parietal art, some of them included on the UNESCO World Heritage list, such as El Castillo and Covalanas. These two caves are currently open to tourism and boast an exceptional archaeological heritage that includes magnificent examples of Palaeolithic cave art. Through a multiyear research project (2020–2022) sponsored by the Government of Cantabria, a precise characterisation of the environmental dynamics of each cave under different meteorological contexts was carried out, as well as an evaluation of the evolution of the impacts of anthropic origin on the underground microclimate under different degrees of influx of visitors on an interannual scale. We aimed to unravel the effects of daily visitor flow on cave environmental stability and offer well-defined recommendations to harmonise conservation priorities with public accessibility based on sustainable tourism management. Once the microclimatic control parameters for the conservation of the paintings, engravings, and supporting rock, such as temperature and CO₂ concentration in the air, were assessed under different seasonal meteorological conditions, a standardised graphic method was implemented based on the frequency distribution of the variations in each parameter, grouped according to the different increasing ranges of daily visits. With this method, it is possible to evaluate, probabilistically and in percentage terms, the microclimatic destabilisation of the cave generated by each group of visitors, taking as a reference the daily variation ranges of each control parameter under natural conditions, i.e., during the days or periods of time in which each cave remained closed to tourist visits. The recommended values of maximum visitor capacity for each cave, in terms of average monthly daily visitor numbers, have been set at 60 and 15 visitors/day for El Castillo and Covalanas caves, respectively. Based on these results, the cave managers are carrying out, from May 2024, a progressive adaptation in the tourist management of these caves until they are fully adapted to the environmental sustainability parameters defined in this study. Full article