Assessment of fear of recurrence in cancer patients in the follow-up phase of the Juan Ramón Jiménez University Hospital in Huelva

Objetivo: Describir los niveles de miedo a la recidiva en los pacientes oncológicos en fase de seguimiento. Metodología Estudio descriptivo transversal llevado a cabo desde junio de 2023 hasta febrero de 2024. Se incluyó una muestra de 315 pacientes adultos, con diagnóstico de cáncer no metastásico en proceso de seguimiento tras al menos 6 meses después de la finalización del tratamiento activo, atendidos en consulta de Oncología del Hospital Universitario Juan Ramón Jiménez de Huelva. Se utilizó el cuestionario para medir el miedo a la recidiva el FCR7-SP. Se obtuvo el permiso del Comité de Ética Provincial. El análisis de datos se realizó con el programa SPSS® v.29.0.1.0. Resultados: Se observó una puntuación media de 26,5 sobre 35 puntos en la escala de FCR7-SP, lo que sugiere un nivel moderado de miedo. La media de edad fue de 57,91 años (DE: ± 11,80). El cáncer más frecuente fue el cáncer de mama (63%) seguido del cáncer colorrectal (20%), en estadio II mayoritariamente. Existe una relación estadísticamente significativa entre el miedo a la recidiva y el género, la nacionalidad, el tratamiento y el tipo de cáncer. Se proponen 5 puntos de corte de la escala FCR7-SP en función de los percentiles obtenidos. Conclusiones: El nivel de miedo a la recidiva en los pacientes oncológicos en fase de seguimiento del Hospital Universitario Juan Ramón Jiménez de Huelva es moderado. Es fundamental identificar los factores que influyen en el nivel de miedo a la recidiva para diseñar intervenciones preventivas y/o el abordaje precoz.Aim: To describe the levels of fear of recurrence in cancer patients in the follow-up phase. Method: Cross-sectional descriptive study carried out from June 2023 to February 2024. A sample of 315 adult patients with a diagnosis of non-metastatic cancer in the process of follow-up at least 6 months after the end of active treatment, seen in the oncology department of the Juan Ramón Jiménez University Hospital in Huelva, was included. The FCR7-SP questionnaire was used to measure fear of recurrence. Permission was obtained from the Provincial Ethics Committee. Data analysis was performed using SPSS software v.29.0.1.0. Results: An average score of 26.5 out of 35 points was observed on the FCR7-SP scale, suggesting a moderate level of fear. Mean age was 57.91 years (SD: ± 11.80). The most frequent cancer was breast cancer (63%) followed by mostly stage II colorectal cancer (20%). There is a statistically significant relationship between fear of recurrence and gender, nationality, treatment and type of cancer. Five cut-off points of the FCR7-SP scale are proposed based on the percentiles obtained. Conclusions: The level of fear of recurrence in cancer patients in the follow-up phase at the Juan Ramón Jiménez University Hospital in Huelva is moderate. It is essential to identify the factors that influence the level of fear of recurrence in order to design preventive interventions and/or an early approach.100,214Q3ESCI1,0Q110,

Personalized glucose forecasting for people with type 1 diabetes using large language models

Background and objective: Type 1 Diabetes (T1D) is an autoimmune disease that requires exogenous insulin via Multiple Daily Injections (MDIs) or subcutaneous pumps to maintain targeted glucose levels. Despite the advances in Continuous Glucose Monitoring (CGM), controlling glucose levels remains challenging. Large Language Models (LLMs) have produced impressive results in text processing, but their performance with other data modalities remains unexplored. The aim of this study is three-fold. First, to evaluate the effectiveness of LLM-based models for glucose forecasting. Second, to compare the performance of different models for predicting glucose in T1D individuals treated with MDIs and pumps. Lastly, to create a personalized approach based on patient-specific training and adaptive model selection. Methods: CGM data from the T1DEXI study were used for forecasting glucose levels. Different predictive models were evaluated using the mean absolute error (MAE) and the root mean squared error and considering the Prediction Horizons (PHs) of 60, 90, and 120 min. Results: For short-term PHs (60 and 90 min), the personalized approach achieved the best results, with an average MAE of 15.7 and 20.2 for MDIs, and a MAE of 15.2 and 17.2 for pumps. For long-term PH (120 min), TIDE obtained an MAE of 19.8 for MDIs, whereas Patch-TST obtained a MAE of 18.5. Conclusion: LLM-based models provided similar MAE values to state-of-the-art models but presented a reduced variability. The proposed personalized approach obtained the best results for short-term periods. Our work contributes to developing personalized glucose prediction models for enhancing glycemic control, reducing diabetes-related complications.161,1894,9Q1Q1SCIE11,