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.

Thrombosis in breast cancer patients on cyclin-dependent kinase inhibitors: Survival impact and predictive factors - A study by the cancer and thrombosis group of the spanish society of medical oncology (SEOM).

Thrombosis may be included in the profile of side effects associated with CDK4/6 inhibitors. Its significance might be greater than reported in randomized clinical trials. To investigate this, a retrospective, multicenter study was conducted.

Neuroendocrine Tumor Heterogeneity Adds Uncertainty to the World Health Organization 2010 Classification: Real-World Data from the Spanish Tumor Registry (R-GETNE).

Background: Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) are a complex family of tumors of widely variable clinical behavior. The World Health Organization (WHO) 2010 classification provided a valuable tool to stratify neuroendocrine neoplasms (NENs) in three prognostic subgroups based on the proliferation index. However, substantial heterogeneity remains within these subgroups, and simplicity sometimes entails an ambiguous and imprecise prognostic stratification.