By the sixth decade of life, nearly one quarter of the population has substantial muscle atrophy, or sarcopenia. Despite the creation of a standardized definition of sarcopenia by the European Working Group on Sarcopenia in Older People, variability may exist in the diagnostic criteria utilized for clinical sarcopenia research. The primary objectives of this review were to characterize diagnostic criteria used for measurement of sarcopenia in original studies, and to describe associations between sarcopenia and important clinical outcomes. We performed a literature review of the term “sarcopenia” in PubMed. Inclusion criteria were English language, original data, a clear and specific definition for diagnosing sarcopenia, and the analysis of sarcopenia’s effect on a clinical outcome. A total of 283 studies met inclusion criteria. More than half of the included sarcopenia investigations were level IV studies (54.1%), while 43.1% provided level II evidence. Under one third (27.6%) of studies examined sarcopenia with regard to surgical outcomes. In terms of diagnostic criteria for sarcopenia, 264 (93.3%) studies used measures of skeletal muscle mass, with dual energy X-ray absorptiometry (DEXA) being the most common modality (43.6%). Sarcopenia was found to be a consistent predictor of chronic disease progression, all-cause mortality, poorer functional outcomes, and postoperative complications. In conclusion, there is substantial evidence that sarcopenia impacts both medical and surgical outcomes. However, current research has utilized heterogeneous diagnostic criteria for sarcopenia. Further efforts to standardize the modalities used to diagnose sarcopenia in clinical research and practice will help strengthen our ability to study this important phenomenon.

Large Language Models (LLMs have the potential to revolutionize clinical medicine by enhancing healthcare access, diagnosis, surgical planning, and education. However, their utilization requires careful, prompt engineering to mitigate challenges like hallucinations and biases. Proper utilization of LLMs involves understanding foundational concepts such as tokenization, embeddings, and attention mechanisms, alongside strategic prompting techniques to ensure accurate outputs. For innovative healthcare solutions, it is essential to maintain ongoing collaboration between AI technology and medical professionals. Ethical considerations, including data security and bias mitigation, are critical to their application. By leveraging LLMs as supplementary resources in research and education, we can enhance learning and support knowledge-based inquiries, ultimately advancing the quality and accessibility of medical care. Continued research and development are necessary to fully realize the potential of LLMs in transforming healthcare.