A systematic review and meta-analysis of 14 randomized controlled trials involving 893 participants found that chronic exercise training for at least 12 weeks significantly increased circulating alpha-Klotho levels. ^[1] The study evaluates the effectiveness of large language models (LLMs) for chronic kidney disease (CKD) screening under zero-shot and few-shot in-context learning settings. ^[2] In patients with chronic kidney disease, a six-month dynamic resistance training program increased Klotho levels by 88.5% and reduced fibroblast growth factor 23 by 30%. ^[3] The authors conclude that circulating alpha-Klotho is best regarded as a promising biomarker of the biological response to exercise rather than a validated therapeutic target. ^[4] The pooled effect size for chronic training on soluble alpha-Klotho was Hedges' g = 1.20 with a 95% confidence interval of 0.85-1.55 and a p-value less than 0.0001. ^[5] LLM performance remains model-dependent and less stable as input complexity increases compared to traditional ML, DL, and TFM models. ^[6] Researchers proposed a framework that uses clinically selected tabular features and structured prompt templates to enable LLM-based inference without task-specific training. ^[7] Results show that LLMs can achieve competitive performance using only a small number of examples, often matching or outperforming traditional approaches in low-data settings. ^[8]