Conference Agenda

Abstract
In Honduras, Physical Medicine and Rehabilitation faces structural limitations that reduce the health system’s capacity to meet growing needs. The objective of this study was to characterize access, availability, and service delivery conditions of Physical Medicine and Rehabilitation services at a public hospital in San Pedro Sula, identifying gaps in coverage, quality, and timeliness of care. Methodology: A descriptive cross-sectional study was conducted during January–February 2025 with 75 participants selected by convenience sampling. Data were collected using a structured questionnaire, and analysis combined descriptive statistics with qualitative thematic categorization of relevant variables. Results: Of the participants, 51% were patients, 26% healthcare professionals, and 23% caregivers, reflecting a comprehensive perspective. Fifty percent of personnel worked in public hospitals, 30% in private clinics, and 20% in community centers or home-based care. Most participants perceived insufficient staff training (60%) and considered infrastructure and equipment inadequate (50%). Additionally, 55% reported the absence of community-based rehabilitation services, while 15% were unaware of their existence. The main challenges identified included shortages of trained personnel (40%), lack of infrastructure and equipment (35%), access limitations (15%), and high treatment costs (10%). Conclusions: Hospital-centered care, limited community availability, and structural barriers negatively affect quality and continuity of care, increasing dependence on caregivers. The active involvement of patients and caregivers underscores the need for a person-centered, comprehensive approach. commendations: Strengthening staff training, improving infrastructure and equipment, decentralizing services, expanding community outreach, and promoting policies that ensure accessibility, continuity, and equity within the national public health system.

Developing countries struggle with functional gastrointestinal issues, which lower quality of life and healthcare use. Traditional epidemiological methods often miss risk profile variability in afflicted groups. This study used unsupervised machine learning to determine Honduran community gastrointestinal health risk profiles for targeted intervention. From September to December 2025, 1,838 Honduran 12-49-year-olds from five departments participated in a cross-sectional survey. Data included sociodemographics, gastrointestinal symptoms, lifestyle, and medical history. K-means, DBSCAN, and hierarchical agglomerative clustering were used. Silhouette coefficient, Davies-Bouldin index, and Calinski-Harabasz index assessed cluster validity. PCA with t-SNE reduced dimension for visualization. The best partitioning was K-means clustering with K=3 (silhouette score: 0.162). There were three risk profiles: (1) High-risk lifestyle cluster (10.2%; n=187) with universal smoking (100%), elevated alcohol consumption (61%), and moderate symptom prevalence; (2) Low-risk cluster (57.3%; n=1,054) with younger individuals with healthy lifestyle habits and minimal symptoms (9.2% abdominal pain); and (3) High-symptom cluster (32.5%; n=597) predominantly female (71.9%) with significant gastrointestinal complaints (67% abdominal pain, 62.6% heartburn) and Machine learning-based clustering enabled individualized primary healthcare intervention design by stratifying the population into clinically meaningful risk profiles. These data support gastrointestinal health program resource allocation optimization.

This longitudinal study examines the cognitive impact of GitHub Copilot on 1,460 engineering students. Using a mixed-methods approach combining cognitive load theory assessments, syntactic retention tests, and development speed metrics, we document a signifi cant inverse relationship between AI assistant dependency and long-term syntactic memory consolidation (r = -0.67, p < 0.001). While intensive Copilot users demonstrated 34.2% faster task completion times initially, they exhibited 41.8% lower syntactic recall in delayed assessments (Week 16) and 53.6% reduced performance in unassisted programming conditions. Analysis revealed that intensive AI use correlates with decreased Germanic cognitive load (r = -0.54, p < 0.001), suggesting reduced deep processing essential for skill acquisition. Domain-specifi c analysis showed a pronounced deterioration in advanced constructs: object-oriented programming (-42.1%), functional programming (-38.7%), and complex data structures (-37.3%). Structural equation modeling confi rmed mediation through Germanic cognitive load (indirect eff ect = -0.33, 95% CI [-0.39, -0.27]), explaining 49% of the total relationship. These fi ndings reveal a critical productivity-learning paradox in AI-assisted programming education, with implications for curriculum design and pedagogical practice in computer science education.

At the Central American Technological University (UNITEC), multiple studies have been carried out in the field of anthropometry, mainly focused on data collection, tool comparison, and information consolidation.

These efforts have enabled the creation of anthropometric databases covering nine departments of Honduras, mostly departmental capitals, which have served as input for the development of reference tables useful in industrial applications.

At the request of the Engineering Faculty, this project followed up on previous studies to consolidate and standardize the information, ensuring the establishment of a repository that can continue to be updated in the future. Three main deliverables were developed: a consolidated Excel database, aligned with the requirements of ISO 15535:2023 and equipped with macros for outlier detection; a Power BI dashboard with interactive visualizations, including a dynamic map of Honduras for exploratory analysis; and a methodological guide designed to standardize measurement protocols and ensure replicability in future research. Taken together, these results strengthen the methodological rigor of anthropometric studies in Honduras and lay the groundwork for the establishment of a sustainable national anthropometric database.

Alzheimer's disease is a progressive brain disorder that affects memory, reasoning ability, and, eventually, the ability to perform simple daily tasks. People diagnosed with this dementia have a life expectancy of up to twenty years from diagnosis. A deep learning-based approach is presented for the classification and diagnosis of Alzheimer's disease using magnetic resonance imaging (MRI) scans. The dataset was obtained from the Kaggle platform, and the metrics of accuracy, recall, and F1 score were applied. Each of these metrics showed a percentage close to 100%, resulting in an average accuracy of 99.52%.

The importance of occupational health and safety in healthcare institutions contributes to improving service quality, reducing operational costs, and strengthening the reputation of these institutions. To analyze the existing literature on occupational health and safety in Ecuadorian hospitals, aiming to identify key concepts, emerging trends, and areas for improvement in this critical field for the well-being of healthcare workers. The study was based on a literature review to analyze occupational health and safety in Ecuadorian hospitals, gathering information from sources such as PubMed, Scopus, Web of Science, SciELO. Topics related to occupational health and risk prevention were selected, applying methods of induction, deduction, and analysis. The literature review reveals that risk prevention in healthcare institutions is complex and multifaceted, combining the importance of strict protocols, the relationship between workplace safety and quality of care, and operational efficiency. The literature review has identified complementary approaches to risk prevention in healthcare institutions, highlighting the importance of integrating these perspectives to develop a more effective management strategy. Comparative analysis has been key in adapting best practices in Ecuador, evaluating critical literature to propose recommendations that improve occupational health.