ABSTRACT Purpose The automated classification of clinical trials and medical literature is increasingly relevant, particularly in oncology, as the volume of publications and trial reports continues to expand. Large Language Models (LLMs) may provide new opportunities for automated diverse classification tasks. In this study, we developed a general-purpose text classification framework using LLMs and evaluated its performance on oncological trial classification tasks. Methods and Materials A general text classification framework with adaptable prompt, model and categories for the classification was developed. The framework was tested with four datasets comprising nine binary classification questions related to oncological trials. Evaluation was conducted using a locally hosted version of Mixtral-8x7B-Instruct v0.1 and three cloud-based LLMs: Mixtral-8x7B-Instruct v0.1, Llama3.1-70B-Instruct, and Qwen-2.5-72B. Results The system consistently produced valid responses with the local Mixtral-8x7B-Instruct model and the Llama3.1-70B-Instruct model. It achieved a response validity rate of 99.70% and 99.88% for the cloud-based Mixtral and Qwen models, respectively. Across all models, the framework achieved an overall accuracy of >94%, precision of >92%, recall of >90%, and an F1-score of >92%. Question-specific accuracy ranged from 86.33% to 99.83% for the local Mixtral model, 85.49% to 99.83% for the cloud-based Mixtral model, 90.50% to 99.83% for the Llama3.1 model, and 77.13% to 99.83% for the Qwen model. Conclusions The LLM-based classification framework exhibits robust accuracy and adaptability across various oncological trial classification tasks. The findings highlight the potential of automated, LLM- driven trial classification systems, which may become increasingly used in oncology.

The increasing integration of digital tools in healthcare has the potential to improve patient outcomes, yet often leads to frustration among users. This study explores the root causes of healthcare professionals' frustrations with technologies in hospitals. Secondary analysis of qualitative interviews with 52 clinicians in Switzerland revealed recurring challenges with digital tools, particularly clinical information systems, that are related to performance, usability, and data accessibility. These issues disrupt clinical workflows, compromise patient care, and can affect the well-being of clinicians. Addressing these issues through collaborative efforts could improve system design, reduce frustration, and enhance healthcare delivery.

This short case covers the recent strategic developments of Barnes & Noble, the largest US book chain, and puts them in the context of its peers worldwide (in the UK, Canada, Germany and France). The perspective of the case is the one of James Daunt, CEO of Barnes & Noble since August 2019. Readers are put in his shoes and are asked to analyze Barnes & Noble's response strategy to an increasingly digital book market. This short case is intended to serve as part of a course session on digitalization strategies or strategic change responding to macro trends. The case can support a full 45-minute class session, or can be integrated into a 90-minute session that also includes theory input by the lecturer. The case questions are open-ended but intended to introduce frameworks to assess strategic change and response strategies to exogenous shocks.