Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.

Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.

Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).

Choosing to learn: The importance of student autonomy in higher education.

Despite strong evidence that autonomy enhances motivation and achievement, few interventions for promoting student autonomy in higher education have been developed and empirically tested. Here, we demonstrate how two autonomy-supportive policies effectively increase classroom attendance and subject mastery. First, in a randomized controlled field study, we explored the effect of allowing students to choose whether to make their attendance mandatory (i.

Unravelling the interactions between small molecules and liposomal bilayers via molecular dynamics and thermodynamic modelling.

Lipid-based drug delivery systems hold immense promise in addressing critical medical needs, from cancer and neurodegenerative diseases to infectious diseases. By encapsulating active pharmaceutical ingredients - ranging from small molecule drugs to proteins and nucleic acids - these nanocarriers enhance treatment efficacy and safety. However, their commercial success faces hurdles, such as the lack of a systematic design approach and the issues related to scalability and reproducibility.

Nonopioid analgesic use in older patients admitted for orthopedic rehabilitation.

Background: Multimodal analgesia (MMA) combines opioids with nonopioid analgesics (NOAs) to mitigate opioid-related adverse events and development of opioid use disorders. Although MMA has become the standard for orthopedic perioperative pain management, guidance is less clear for the approximately 15% of patients who go on to require inpatient orthopedic rehabilitation (IOR) postoperatively. The IOR population tends to be older and frailer and hence likely more vulnerable to adverse events.