Leveraging Large Language Models for Precision Monitoring of Chemotherapy-Induced Toxicities: A Pilot Study with Expert Comparisons and Future Directions.

Introduction: Large Language Models (LLMs), such as the GPT model family from OpenAI, have demonstrated transformative potential across various fields, especially in medicine. These models can understand and generate contextual text, adapting to new tasks without specific training. This versatility can revolutionize clinical practices by enhancing documentation, patient interaction, and decision-making processes.

Update on the management of older patients with pancreatic adenocarcinoma: a perspective from medical oncology.

In the context of pancreatic cancer, surgical intervention is typically recommended for localized tumours, whereas chemotherapy is the preferred approach in the advanced and/or metastatic setting. However, pancreatic cancer is closely linked to ageing, with an average diagnosis at 72 years. Paradoxically, despite its increased occurrence among older individuals, this population is often underrepresented in clinical studies, complicating the decision-making process.

Incidence, risk factors, and evolution of venous thromboembolic events in patients diagnosed with pancreatic carcinoma and treated with chemotherapy on an outpatient basis.

Background: Pancreatic carcinoma is one of the tumors associated with a higher risk for thromboembolic events, with incidence rates ranging from 5% to 41% in previous retrospective series.

Patients And Methods: We conducted a retrospective study in eleven Spanish hospitals that included 666 patients diagnosed with pancreatic carcinoma (any stage) between 2008 and 2011 and treated with chemotherapy. The main objective was to evaluate the incidence of venous thromboembolic events (VTE) in this population, as well as potential risk factors for thrombosis.