The Impact of Temperature on Extracting Information From Clinical Trial Publications Using Large Language Models.

Introduction The application of natural language processing (NLP) for extracting data from biomedical research has gained momentum with the advent of large language models (LLMs). However, the effect of different LLM parameters, such as temperature settings, on biomedical text mining remains underexplored and a consensus on what settings can be considered "safe" is missing. This study evaluates the impact of temperature settings on LLM performance for a named entity recognition and a classification task in clinical trial publications.

Metastatic Versus Localized Disease as Inclusion Criteria That Can Be Automatically Extracted From Randomized Controlled Trials Using Natural Language Processing.

Purpose: Extracting inclusion and exclusion criteria in a structured, automated fashion remains a challenge to developing better search functionalities or automating systematic reviews of randomized controlled trials in oncology. The question "Did this trial enroll patients with localized disease, metastatic disease, or both?" could be used to narrow down the number of potentially relevant trials when conducting a search.

Methods: Six hundred trials from high-impact medical journals were classified depending on whether they allowed for the inclusion of patients with localized and/or metastatic disease.

Predicting the sample size of randomized controlled trials using natural language processing.

Objectives: Extracting the sample size from randomized controlled trials (RCTs) remains a challenge to developing better search functionalities or automating systematic reviews. Most current approaches rely on the sample size being explicitly mentioned in the abstract. The objective of this study was, therefore, to develop and validate additional approaches.

Prototyping and Experimental Analysis of Active Offloading Footwear for Patients With Diabetes Using an Array of Magnetorheological Fluid-Based Modules.

Background: Diabetic foot ulceration is a serious challenge worldwide which imposes an immense risk of lower extremity amputation and in many cases may lead to the death. The presented work focuses on the offloading requirements using an active approach and considers the use of magnetorheological fluid-based modules to redistribute high plantar pressures (PPs).

Methods & Results: Experimentation validated a single module with a threshold peak pressure of 450 kPa, whereas an offloading test with a three-module array and complete footwear validated a maximum pressure reduction of 42.