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.

Assessment of hard tissue changes after horizontal guided bone regeneration with the aid of deep learning CBCT segmentation.

Objectives: To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.

Materials And Methods: The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR.

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.

SMILE-stereotactic multiple fraction radiotherapy for non-spine bone metastases: study protocol for a multicenter, open-label phase III randomized controlled trial.

Background: The SMILE study addresses a significant need in palliative oncology by evaluating the non-inferiority of a shortened, 3-fraction stereotactic body radiotherapy (SBRT) schedule against the traditional 5-fraction approach for non-spine bone metastases in terms of pain control. Optimizing SBRT could significantly enhance the quality of life for patients by providing effective pain relief while minimizing treatment sessions.

Methods: This international, multicenter phase III trial will randomize 162 patients to receive either a 3-fraction regimen (9 Gy per fraction) or a standard 5-fraction regimen (7 Gy per fraction).

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.