José Alberto Benítez-Andrades

This qualitative case study examines how a teacher orchestrated generative AI within an authentic, public-facing task, using a secondary-education classroom as a micro-laboratory with transfer potential to higher education and initial teacher education. Over seven lessons in a Digitalisation course (15–16-year-olds), students used ChatGPT to support the design of Instagram infographics and captions under explicit rules for verification, rewriting and responsible use. Evidence from teacher planning/diary notes, rubric-based assessment, and students’ open reflections (N = 30; thematically coded) shows that governance and ethics can be made visible through structured scaffolding, moderation choices, and assessment. Findings are synthesised into a reusable four-phase orchestration model: (1) risk mapping, (2) task and prompt design, (3) live orchestration, and (4) assessment and reflection. We discuss implications for designing authentic tasks, assessment, and the governance of generative AI in higher education and teacher preparation.

Subacute low back pain (LBP) is a highly prevalent condition and a major contributor to disability and health care burden. Early identification of individu

Background: Subacute low back pain (LBP) is a highly prevalent condition and a major contributor to disability and health care burden. Early identification of individuals at risk of poor functional recovery is essential to support decision-making in primary care. Although prior research has identified relevant clinical and psychosocial predictors, the application of machine learning techniques for modeling disability and pain outcomes in this population remains limited. Methods: We conducted a prospective observational study involving 92 adult patients with subacute LBP attending primary care physiotherapy services. At baseline, participants completed standardized assessments of disability (percentage of perceived limitation in daily activities), pain intensity (numeric rating scale), self-efficacy (confidence to perform activities despite pain), fear of movement, and psychosocial risk classification. Predictive models were developed for categorical outcomes of functional disability and pain intensity at discharge and at three-month follow-up using Gaussian Naive Bayes, Complement Naive Bayes, k-Nearest Neighbors, and Decision Tree classifiers. Models were trained under three feature set configurations (intrinsic, extrinsic, combined) and evaluated with and without oversampling techniques (RandomOverSampler, Synthetic Minority Oversampling Technique). Performance metrics included accuracy and F1-score. Feature importance analysis was performed using SelectKBest and ExtraTreesClassifier. Results: The best results were obtained with the Decision Tree classifier using the combined feature set (accuracy = 0.81, F1-score = 0.80). Baseline pain intensity was the most relevant predictor for disability outcomes, while baseline disability was most influential for pain intensity predictions. Psychosocial factors—self-efficacy, kinesiophobia, and psychosocial risk—showed moderate contributions. Age and sex had minimal predictive impact. Conclusions: Machine learning models, particularly Decision Tree classifiers, can accurately predict functional disability and pain intensity in patients with subacute LBP using routinely collected clinical and psychosocial data. Such models could support early risk stratification and facilitate the development of clinical decision support tools for personalized care in primary care physiotherapy. Trial registration: ClinicalTrials.gov NCT05860426. Registered on April 26, 2023.

Objectives: Routinely collected data could support risk stratification after alcohol-related hospitalisation, but inconsistent diagnostic phenotyping and temporally invalid designs threaten model validity. We aimed to develop and temporally evaluate interpretable models for subsequent same-hospital admission recorded in the available extract after alcohol-related admissions. Methods: We analysed anonymised hospital episodes from 2009–2024. Adult alcohol-related admissions were identified by the union of local text labels and ICD-9/10-compatible alcohol codes. At each discharge, models predicted the next same-hospital admission recorded in the extract within 30, 90, and 365 days using only contemporaneous and prior information. Logistic regression, random forest, and histogram gradient boosting were developed using admissions through 2020 and evaluated on later admissions. Discrimination, calibration, Brier scores, and highest-risk-decile performance were estimated with 95% confidence intervals from patient-clustered bootstrap samples. Results: After excluding 58 records without usable admission or discharge dates and 36 records with missing age or age below 18 years, 18,335 alcohol-related index admissions from 10,478 patients were analysed. The text-only rule identified 6,652 patients, compared with 10,478 using text or ICD codes; source use changed completely from text labels in 2009–2019 to ICD codes in 2020–2024. In temporal evaluation, random-forest ROC-AUC was 0.659 (95% CI 0.631–0.683) at 30 days and 0.659 (0.637–0.681) at 90 days. At 365 days, logistic regression and random forest performed similarly (both ROC-AUC 0.659); logistic regression was well calibrated (intercept −0.044, slope 0.906). Its highest-risk decile had 57.4% positive predictive value and captured 18.3% of recorded subsequent admissions. Conclusions: The operational cohort-definition rule materially changed the study population. Temporally evaluated models provided modest risk stratification but no complex algorithm clearly outperformed logistic regression. The observed discrimination and decision-concentration metrics do not support direct clinical deployment. External evaluation and richer clinical and social predictors are required before clinical use.

Background: Dating apps for men who have sex with men (MSM) have facilitated unprotected sexual encounters and practices. In addition, drug use during such encounters is widespread among MSM. Traditionally, these populations have been studied in order to relate these facts to their socioeconomic characteristics and not from the perspective of the structure of their relationships, an aspect included in all nursing metaparadigms. Aim: Describe the structure of the MSM affiliation network and their dating apps. Methods: This was a descriptive cross‐sectional study with 32 participants recruited online through the apps. The data used by this article come from the same sample and the same questionnaires as a previously conducted study; full details are provided in the Methods section. Results: MSM demonstrated varying degrees, engaging with 1–5 applications (normalized degree: 0.200–0.494), while venue popularity spanned from 1 to 32 (normalized degree: 0.031–0.500). The core of the network, including two applications and 16 central MSM units, exhibited a higher density, that is, a high number of connections (0.593), compared to the periphery (0.050), indicating significant centralization. Conclusion: The sexual affiliation network of MSM forms a cohesive, extensive network, with higher app usage affinity seen among individuals who use drugs, are from different birth countries, engage in group sex, or identify with nonhomosexual orientations.

Objective assessment of balance during childhood is essential for supporting motor development, preventing falls, and identifying potential impairments. Traditional clinical tests, such as the Flamingo and balance beam, are widely used but remain limited by subjectivity and lack of precision. Accelerometry offers quantitative measures, and its integration with machine learning models provides an opportunity to explore predictive assessment in pediatric populations. This study evaluated the feasibility of predicting clinical balance test outcomes in schoolchildren using accelerometric data from both static and dynamic tasks. A cross-sectional study was conducted with 90 children aged 6 to 12 years. Accelerometric signals were recorded in three axes and magnitude during static tasks (eyes closed, eyes open, and unstable surface) and during gait. Outcomes included the number of supports in the Flamingo test and the distance covered in the balance beam test. Several machine learning models, including linear regression, penalized regression, k-nearest neighbors, support vector regression, and random forest, were applied using 5-fold cross-validation. Models showed modest but consistent predictive accuracy for the Flamingo test, particularly with static tasks, with random forest, support vector regression, and k-nearest neighbors performing best. Prediction of balance beam outcomes was poor across all models. These findings suggest that accelerometry-based machine learning is feasible for predicting balance performance in children, especially for the Flamingo test, supporting its potential as a digital tool for screening and educational health applications.

Background/Objectives: During the Coronavirus Disease 2019 (COVID-19) pandemic, various safety measures were implemented in elderly care facilities in Spain. These measures led to a reduction in physical activity and increased supervision of residents, often resulting in the suspension of outings from the facility. The objective of this study was to assess the influence of COVID-19 preventive measures on the number and characteristics of falls among elderly individuals living in a residential care facility in Spain. Methods: A retrospective longitudinal observational study was conducted from 2018 to 2021. Over these four years, data related to falls were collected from a residential care facility for the elderly. Both patient characteristics and fall characteristics were recorded. Results: The average age of the 48 residents continuously institutionalized between 2018 and 2021 was 85.8 ± 5.1 years. A total of 364 falls occurred during the study period, with 68% of them taking place in 2019 and 2020. Although the number of falls increased during the COVID-19 pandemic, the characteristics of the falls did not change. However, residents who experienced falls were increasingly accompanied at the time of the event. Conclusions: Based on the data collected from the elderly care facility analyzed in this study, falls increased during the COVID-19 pandemic, but the measures implemented in residential care facilities do not appear to have altered the characteristics of the falls, except for the greater presence of companionship.

Este trabajo presenta un sistema para la representación adaptativa del estado de múltiples plataformas robóticas en un entorno compartido. El sistema se basa en la metodología CAD2SLAM para realizar una proyección de las posiciones individuales de cada robot sobre un mapa base común empleando una rejilla de proyección no-rígida ajustada mediante un conjunto de puntos de correspondencia y un proceso de optimización de mínimos cuadrados. Esto permite representar de forma coherente y comprensible la posición relativa de cada robot, independientemente del tipo de sensores o mapa que este utilice. El operador puede supervisar el estado global del sistema multi-robot de forma más intuitiva y eficiente, sin alterar la arquitectura interna ni el funcionamiento de los robots. Validado en entornos simulados y reales, demuestra robustez y ejecución en tiempo real. La principal novedad es el uso de un mapa base común, que facilita la cooperación entre plataformas heterogéneas y la interpretación visual del entorno.