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Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants.
Advanced Line-of-Sight (LOS) model for communicating devices in modern indoor environment.
Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives.
Advancing Nutritional Science: Contemporary Perspectives on Diet’s Role in Metabolic Health and Disease Prevention.
Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix.
An Analytical Framework for Innovation Determinants and Their Impact on Business Performance.
Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach.
An Anchor-Based Localization in Underwater Wireless Sensor Networks for Industrial Oil Pipeline Monitoring.
Anthocyanins: what do we know until now?
Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.
Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos.
Análisis crítico sobre el perfil de salida del bachillerato ecuatoriano. Una mirada desde el método de aprendizaje basado en proyectos.
The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment.
An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features.
An Artificial Neural Network Model for Water Quality and Water Consumption Prediction.
An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation.
Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.
Association between blood cortisol levels and numerical rating scale in prehospital pain assessment.
Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach.
Betalains: The main bioactive compounds of Opuntia spp and their possible health benefits in the Mediterranean diet.
Blockchain based transparent and reliable framework for wheat crop supply chain.
Blockchain-Modeled Edge-Computing-Based Smart Home Monitoring System with Energy Usage Prediction.
Botnet detection in internet of things using stacked ensemble learning model.
Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning.
Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.
Can alpha‐linolenic acid be a modulator of “cytokine storm,” oxidative stress and immune response in SARS‐CoV‐2 infection?
Can the phenolic compounds of Manuka honey chemosensitize colon cancer stem cells? A deep insight into the effect on chemoresistance and self-renewal.
Carotenoids Intake and Cardiovascular Prevention: A Systematic Review.
Client engagement solution for post implementation issues in software industry using blockchain.
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs.
A Comparison of the Clinical Characteristics of Short-, Mid-, and Long-Term Mortality in Patients Attended by the Emergency Medical Services: An Observational Study.
Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems.
A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.
Consortium Framework Using Blockchain for Asthma Healthcare in Pandemics.
Contextual Urdu Lemmatization Using Recurrent Neural Network Models.
Correction: Prediction of leukemia peptides using convolutional neural network and protein compositions.
Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques.
Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential.
Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection.
Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance.
Deep learning model for detection of brown spot rice leaf disease with smart agriculture.
Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble.
Design and Development of Smart Weight Measurement, Lateral Turning and Transfer Bedding for Unconscious Patients in Pandemics.
Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.
A Detectability Analysis of Retinitis Pigmetosa Using Novel SE-ResNet Based Deep Learning Model and Color Fundus Images.
Detecting Cyberattacks to Federated Learning on Software-Defined Networks.
Detecting Pragmatic Ambiguity in Requirement Specification Using Novel Concept Maximum Matching Approach Based on Graph Network.
Detection of cotton crops diseases using customized deep learning model.
Development Agencies and Local Governments—Coexistence within the Same Territory.
DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network.
Diagnosing Training Needs in European Tourism SMEs: The TC-NAV Project for Managing and Overcoming Virulent Crises.
Diagnosing epileptic seizures using combined features from independent components and prediction probability from EEG data.
Distributed Denial of Service Attack Detection in Network Traffic Using Deep Learning Algorithm.
DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents.
Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city.
Efecto de un entrenamiento propioceptivo para prevenir el riesgo de caída en adultos mayores.
An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.
Efficient deep learning-based approach for malaria detection using red blood cell smears.
Emotion Detection Using Facial Expression Involving Occlusions and Tilt.
Emotional Management in Journalism and Communication Studies.
Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.
Energy-Aware Live VM Migration Using Ballooning in Cloud Data Center.
Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.
Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.
Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning.
Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification.
Ensemble stacked model for enhanced identification of sentiments from IMDB reviews.
Estudio comparativo entre metodología de aula invertida y metodología tradicional en clases de español, inglés y matemáticas.
Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review.
Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean.
Evolving epidemiology, clinical features, and genotyping of dengue outbreaks in Bangladesh, 2000–2024: a systematic review.
FairHealth: Long-Term Proportional Fairness-Driven 5G Edge Healthcare in Internet of Medical Things.
Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms.
Flavan-3-ols and Vascular Health: Clinical Evidence and Mechanisms of Action.
Forecasting of Post-Graduate Students’ Late Dropout Based on the Optimal Probability Threshold Adjustment Technique for Imbalanced Data.
Formal modeling and analysis of security schemes of RPL protocol using colored Petri nets.
Funcionalidad motriz, estado nutricional e índices antropométricos de riesgo cardiometabólico en adolescentes chilenos de 12 a 15 años.
Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices.
Hierarchical Attention Module-Based Hotspot Detection in Wafer Fabrication Using Convolutional Neural Network Model.
Human‐based new approach methodologies to accelerate advances in nutrition research.
Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization.
Image Watermarking Using Least Significant Bit and Canny Edge Detection.
Impact of Innovation-Oriented Human Resource on Small and Medium Enterprises’ Performance.
Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective.
Implementation of photovoltaic energy, sustainability, economic and social development in a Higher Education Institution in Brazil.
Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities.
Inequalities and Asymmetries in the Development of Angola’s Provinces: The Impact of Colonialism and Civil War.
Influence of E-learning training on the acquisition of competences in basketball coaches in Cantabria.
Instrumentalization of a Model for the Evaluation of the Level of Satisfaction of Graduates under an E-Learning Methodology: A Case Analysis Oriented to Postgraduate Studies in the Environmental Field.
Integration of Sustainable Criteria in the Development of a Proposal for an Online Postgraduate Program in the Projects Area.
An Intelligent Dual-Axis Solar Tracking System for Remote Weather Monitoring in the Agricultural Field.
IoT Enabled Intelligent Stick for Visually Impaired People for Obstacle Recognition.
IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System.
Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?
La comunidad docente y las competencias digitales: la formación a lo largo de la vida.
Lifestyle Factors Associated with Children’s and Adolescents’ Adherence to the Mediterranean Diet Living in Mediterranean Countries: The DELICIOUS Project.
Mediterranean Diet and Sleep Features: A Systematic Review of Current Evidence.
Methodology and content for the design of basketball coach education programs: a systematic review.
Mitigating 5G security challenges for next-gen industry using quantum computing.
Model Driven Approach for Efficient Flood Disaster Management with Meta Model Support.
Modelo geoespacial para priorizar los factores de riesgo ambiental de las comunidades sin alcantarillado sanitario en la cuenca del Río Grande de Loíza en Puerto Rico.
Molecular epidemiology, transmission and clinical features of 2022‐mpox outbreak: A systematic review.
Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring.
NLP-Based Application for Analyzing Private and Public Banks Stocks Reaction to News Events in the Indian Stock Exchange.
Natural Language Processing-Based Software Testing: A Systematic Literature Review.
A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization.
A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.
A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization.
Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.
Novel model to authenticate role-based medical users for blockchain-based IoMT devices.
Novel transfer learning approach for hand drawn mathematical geometric shapes classification.
Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults.
Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements.
An Optimized Intelligent Computational Security Model for Interconnected Blockchain-IoT System & Cities.
An Optimized Open Pit Mine Application for Limestone Quarry Production Scheduling to Maximize Net Present Value.
Organizational Culture Assessment Based on a Values-Based Coaching Program for Strategic Level Employees: The Case of GEDEME, Cuba.
PRUS: Product Recommender System Based on User Specifications and Customers Reviews.
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments.
Perseguindo o inédito viável: a pedagogia freiriana, a necesssidade da linguagem inclusiva e a denúncia à neolíngua do generismo queer.
Pneumonia Detection Using Chest Radiographs With Novel EfficientNetV2L Model.
População infantil e adolescente nas ruas e estudantes estrangeiros: impactos interculturais no desenvolvimento e no acesso às escolas.
Prediction of leukemia peptides using convolutional neural network and protein compositions.
Prediction β-Thalassemia carriers using complete blood count features.
Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study.
Prehospital qSOFA, mSOFA, and NEWS2 performance for sepsis prediction: A prospective, multi-center, cohort study.
Prevalence and genetic diversity of rotavirus in Bangladesh during pre-vaccination period, 1973-2023: a meta-analysis.
Prevalence and impact of long COVID-19 among patients with diabetes and cardiovascular diseases in Bangladesh.
Prickly pear fruits from "Opuntia ficus-indica" varieties as a source of potential bioactive compounds in the Mediterranean diet.
Pupilometer efficacy in monitoring anxiety in undergraduate medical students during high-fidelity clinical simulation.
PyDEMATEL: A Python-based tool implementing DEMATEL and fuzzy DEMATEL methods for improved decision making.
Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.
Real Word Spelling Error Detection and Correction for Urdu Language.
Relación entre funcionalidad motriz y factores antropométricos de riesgo cardio metabólico en bomberos de la región de Valparaíso, Chile.
Resilience Optimization of Post-Quantum Cryptography Key Encapsulation Algorithms.
Risk Factors for Eating Disorders in University Students: The RUNEAT Study.
Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease.
Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.
SARSMutOnto: An Ontology for SARS-CoV-2 Lineages and Mutations.
The Scope of Technostress and Care of The Self on Journalists During the Pandemic.
Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective.
A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking Prediction.
Smart Fault Monitoring and Normalizing of a Power Distribution System Using IoT.
Smart Physiotherapy: Advancing Arm-Based Exercise Classification with PoseNet and Ensemble Models.
Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.
Software Cost and Effort Estimation: Current Approaches and Future Trends.
Spare Parts Forecasting and Lumpiness Classification Using Neural Network Model and Its Impact on Aviation Safety.
StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.
Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models.
Sustentabilidade em sistemas de segurança do trabalho na construção civil.
A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions.
Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.
Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape.
Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19.
Tensiomyography, functional movement screen and counter movement jump for the assessment of injury risk in sport: a systematic review of original studies of diagnostic tests.
Threatening URDU Language Detection from Tweets Using Machine Learning.
Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques.
Towards Security Mechanism in D2D Wireless Communication: A 5G Network Approach.
Transformer-based ECG classification for early detection of cardiac arrhythmias.
Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band.
Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence.
Underrated aspects of a true Mediterranean diet: understanding traditional features for worldwide application of a “Planeterranean” diet.
Underwater Wireless Sensor Networks: Enabling Technologies for Node Deployment and Data Collection Challenges.
Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis.
Use of Fuzzy Approach Methodology and Consensus in Creating a Hierarchy of Satisfaction for Measurement Criteria: Application to Online Training Actions Directed at Classification by Key Competency Profiles in Sales Supervision (SPV) within the Automotive.
The Use of Neuroimaging to Assess Associations Among Diet, Nutrients, Metabolic Syndrome, and Alzheimer’s Disease.
Virtual histopathology methods in medical imaging - a systematic review.
Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches.
Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.
White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.
A deep learning approach for Named Entity Recognition in Urdu language.
An enhanced approach for predicting air pollution using quantum support vector machine.
An enhanced opportunistic rank-based parent node selection for sustainable & smart IoT networks.
A ferramenta SWOT na gestão escolar.
An improved deep convolutional neural network-based YouTube video classification using textual features.
A internacionalização em casa na pós-graduação na América Latina e Caribe na modalidade a distância.
A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.
A real-time air-writing model to recognize Bengali characters.
A systematic review of deep learning methods for community detection in social networks.
A violência doméstica no município da Cela, província do Cuanza-Sul –Angola: um fenómeno que tem preocupado o governo e a sociedade.
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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence
Gender classification plays a vital role in various applications, particularly in security and healthcare. While several biometric methods such as facial recognition, voice analysis, activity monitoring, and gait recognition are commonly used, their accuracy and reliability often suffer due to challenges like body part occlusion, high computational costs, and recognition errors. This study investigates gender classification using gait data captured by Ultra-Wideband radar, offering a non-intrusive and occlusion-resilient alternative to traditional biometric methods. A dataset comprising 163 participants was collected, and the radar signals underwent preprocessing, including clutter suppression and peak detection, to isolate meaningful gait cycles. Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. The study highlights the effectiveness of Ultra-wideband radar and the proposed transformation framework in advancing robust gender classification.
Adil Ali Saleem mail , Hafeez Ur Rehman Siddiqui mail , Muhammad Amjad Raza mail , Sandra Dudley mail , Julio César Martínez Espinosa mail ulio.martinez@unini.edu.mx, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Isabel de la Torre Díez mail ,
Saleem
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Background: Physical activity in children and adolescents represents one of the most important lifestyle factors to determine current and future health. Aim: The aim of the study is to assess the lifestyle and dietary factors linked to physical activity in younger populations across five countries in the Mediterranean region. Design: A total of 2,011 parents of children and adolescents (age range 6–17 years) participating to a preliminary survey of the DELICIOUS project were investigated to determine children's adequate physical activity level (identified using the short form of the international physical activity questionnaire) as well as diet quality parameters [measured as Youth-Healthy Eating Index (Y-HEI)] and eating and lifestyle factors (i.e., meal habits, sleep duration, screen time, etc.). Logistic regression analyses were performed to assess the odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between variables of interest. Results: Younger children of younger parents currently working had higher rates and probability to have adequate physical activity. Multivariate analysis showed that children and adolescents who had breakfast (OR = 1.88, 95% CI: 1.38, 2.56) and often ate with their family (OR = 1.80, 95% CI: 0.90, 3.61) were more likely to have an adequate level of physical activity. Children and adolescents who reported a sleep duration (8–10 h) closest to the recommended one were significantly more likely to achieve adequate levels of physical activity (OR = 1.88, 95% CI: 1.38, 2.56). Conversely, those with more than 4 h of daily screen time were less likely to engage in adequate physical activity (OR = 0.77, 95% CI: 0.54, 1.10). Furthermore, children and adolescents in the highest tertile of YEHI scores showed a 60% greater likelihood of engaging in adequate physical activity (OR = 1.60, 95% CI: 1.27, 2.01). Conclusion: These results emphasize the importance of promoting healthy diet and lifestyle habits, including structured and high quality shared meals, sufficient sleep, and screen time moderation, as key strategies to support active behaviors in younger populations. Future interventions should focus on reinforcing these behaviors through parental guidance and community-based initiatives to foster lifelong healthy habits.
Alice Rosi mail , Francesca Scazzina mail , Maria Antonieta Touriz Bonifaz mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Achraf Ammar mail , Khaled Trabelsi mail , Osama Abdelkarim mail , Mohamed Aly mail , Evelyn Frias-Toral mail , Juancho Pons mail , Laura Vázquez-Araújo mail , Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Lorenzo Monasta mail , Nunzia Decembrino mail , Ana Mata mail , Adrián Chacón mail , Pablo Busó mail , Giuseppe Grosso mail ,
Rosi
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A systematic review of deep learning methods for community detection in social networks
Introduction: The rapid expansion of generated data through social networks has introduced significant challenges, which underscores the need for advanced methods to analyze and interpret these complex systems. Deep learning has emerged as an effective approach, offering robust capabilities to process large datasets, and uncover intricate relationships and patterns. Methods: In this systematic literature review, we explore research conducted over the past decade, focusing on the use of deep learning techniques for community detection in social networks. A total of 19 studies were carefully selected from reputable databases, including the ACM Library, Springer Link, Scopus, Science Direct, and IEEE Xplore. This review investigates the employed methodologies, evaluates their effectiveness, and discusses the challenges identified in these works. Results: Our review shows that models like graph neural networks (GNNs), autoencoders, and convolutional neural networks (CNNs) are some of the most commonly used approaches for community detection. It also examines the variety of social networks, datasets, evaluation metrics, and employed frameworks in these studies. Discussion: However, the analysis highlights several challenges, such as scalability, understanding how the models work (interpretability), and the need for solutions that can adapt to different types of networks. These issues stand out as important areas that need further attention and deeper research. This review provides meaningful insights for researchers working in social network analysis. It offers a detailed summary of recent developments, showcases the most impactful deep learning methods, and identifies key challenges that remain to be explored.
Mohamed El-Moussaoui mail , Mohamed Hanine mail , Ali Kartit mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Helena Garay mail helena.garay@uneatlantico.es, Isabel de la Torre Díez mail ,
El-Moussaoui
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Transformer-based ECG classification for early detection of cardiac arrhythmias
Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. This study presents a Transformer-based deep learning framework for automated ECG classification, integrating advanced preprocessing, feature selection, and dimensionality reduction techniques to improve model performance. The pipeline begins with signal preprocessing, where raw ECG data are denoised, normalized, and relabeled for compatibility with attention-based architectures. Principal component analysis (PCA), correlation analysis, and feature engineering is applied to retain the most informative features. To assess the discriminative quality of the selected features, t-distributed stochastic neighbor embedding (t-SNE) is used for visualization, revealing clear class separability in the transformed feature space. The refined dataset is then input to a Transformer- based model trained with optimized loss functions, regularization strategies, and hyperparameter tuning. The proposed model demonstrates strong performance on the MIT-BIH benchmark dataset, showing results consistent with or exceeding prior studies. However, due to differences in datasets and evaluation protocols, these comparisons are indicative rather than conclusive. The model effectively classifies ECG signals into categories such as Normal, atrial premature contraction (APC), ventricular premature contraction (VPC), and Fusion beats. These results underscore the effectiveness of Transformer-based models in biomedical signal processing and suggest potential for scalable, automated ECG diagnostics. However, deployment in real-time or resource-constrained settings will require further optimization and validation.
Sunnia Ikram mail , Amna Ikram mail , Harvinder Singh mail , Malik Daler Ali Awan mail , Sajid Naveed mail , Isabel De la Torre Díez mail , Henry Fabian Gongora mail henry.gongora@uneatlantico.es, Thania Chio Montero mail ,
Ikram
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Association between blood cortisol levels and numerical rating scale in prehospital pain assessment
Background Nowadays, there is no correlation between levels of cortisol and pain in the prehospital setting. The aim of this work was to determine the ability of prehospital cortisol levels to correlate to pain. Cortisol levels were compared with those of the numerical rating scale (NRS). Methods This is a prospective observational study looking at adult patients with acute disease managed by Emergency Medical Services (EMS) and transferred to the emergency department of two tertiary care hospitals. Epidemiological variables, vital signs, and prehospital blood analysis data were collected. A total of 1516 patients were included, the median age was 67 years (IQR: 51–79; range: 18–103) with 42.7% of females. The primary outcome was pain evaluation by NRS, which was categorized as pain-free (0 points), mild (1–3), moderate (4–6), or severe (≥7). Analysis of variance, correlation, and classification capacity in the form area under the curve of the receiver operating characteristic (AUC) curve were used to prospectively evaluate the association of cortisol with NRS. Results The median NRS and cortisol level are 1 point (IQR: 0–4) and 282 nmol/L (IQR: 143–433). There are 584 pain-free patients (38.5%), 525 mild (34.6%), 244 moderate (16.1%), and 163 severe pain (10.8%). Cortisol levels in each NRS category result in p < 0.001. The correlation coefficient between the cortisol level and NRS is 0.87 (p < 0.001). The AUC of cortisol to classify patients into each NRS category is 0.882 (95% CI: 0.853–0.910), 0.496 (95% CI: 0.446–0.545), 0.837 (95% CI: 0.803–0.872), and 0.981 (95% CI: 0.970–0.991) for the pain-free, mild, moderate, and severe categories, respectively. Conclusions Cortisol levels show similar pain evaluation as NRS, with high-correlation for NRS pain categories, except for mild-pain. Therefore, cortisol evaluation via the EMS could provide information regarding pain status.
Raúl López-Izquierdo mail , Elisa A. Ingelmo-Astorga mail , Carlos del Pozo Vegas mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Ancor Sanz-García mail , Francisco Martín-Rodríguez mail ,
López-Izquierdo