Aging coincides with a decline in LLMS. Preserving LLMS may be considered a very important determinant of functional independence in the elderly. To maintain LLMS the question arises whether habitual physical activities (HPA) can prevent a decline in LLMS. This review aims to determine the relationship between HPA throughout life and LLMS above age 50. Using relevant databases and keywords, 70 studies that met the inclusion criteria were reviewed and where possible, a meta-analysis was performed. The main findings are: (1) the present level of HPA is positively related to LLMS; (2) HPA in the past has little effect on present LLMS; (3) HPA involving endurance have less influence on LLMS compared to HPA involving strength; (4) people with a stable habitually physically active life are able to delay a decline in LLMS. In conclusion, to obtain a high amount of LLMS during aging, it is important to achieve and maintain a high level of HPA with mainly muscle-strengthening activities.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.archger.2011.04.018 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The Impact of Temperature on Extracting Information From Clinical Trial Publications Using Large Language Models.
Cureus
December 2024
Department of Radiation Oncology, Cantonal Hospital Winterthur, Winterthur, CHE.
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.
View Article and Find Full Text PDFPurpose: We present an updated study evaluating the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models.
Methods: A set of 100 multiple choice radiation oncology physics questions, previously created by a well-experienced physicist, was used for this study. The answer options of the questions were randomly shuffled to create "new" exam sets.
Programming Chatbots Using Natural Language: Generating Cervical Spine MRI Impressions.
Cureus
September 2024
Department of Radiology, George Washington University School of Medicine and Health Sciences, Washington, D.C., USA.
Purpose: The utility of machine learning, specifically large language models (LLMs), in the medical field has gained considerable attention. However, there is a scarcity of studies that focus on the application of LLMs in generating custom subspecialty radiology impressions. The primary objective of this study is to evaluate and compare the performance of multiple LLMs in generating specialized, accurate, and clinically useful radiology impressions for degenerative cervical spine MRI reports.
View Article and Find Full Text PDFEnhancing early detection of cognitive decline in the elderly: a comparative study utilizing large language models in clinical notes.
EBioMedicine
November 2024
Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.
Article Synopsis
- The study investigates the effectiveness of large language models (LLMs) like GPT-4 and Llama 2 in identifying cognitive decline from real electronic health records (EHRs), comparing them with traditional models.
- Conducted at Mass General Brigham, researchers analyzed clinical notes from patients diagnosed with mild cognitive impairment, using various approaches to optimize LLM performance and create an ensemble model that combined different methods.
- The findings showed that while GPT-4 was more accurate than Llama 2, it still didn't surpass traditional models; however, an ensemble model significantly outperformed all others in key evaluation metrics.
Use of SNOMED CT in Large Language Models: Scoping Review.
JMIR Med Inform
October 2024
Department of Nursing Science, Research Institute of Nursing Science, Chungbuk National University, Cheongju, Republic of Korea.
Background: Large language models (LLMs) have substantially advanced natural language processing (NLP) capabilities but often struggle with knowledge-driven tasks in specialized domains such as biomedicine. Integrating biomedical knowledge sources such as SNOMED CT into LLMs may enhance their performance on biomedical tasks. However, the methodologies and effectiveness of incorporating SNOMED CT into LLMs have not been systematically reviewed.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!