## 2 Literature Review  
LLMs have recently emerged as powerful tools for automating the extraction of structured information from unstructured clinical texts. Traditional manual data extraction for evidence synthesis remains labor-intensive and prone to human error, creating significant workflow bottlenecks [47]. Recent studies highlight the promising capabilities of LLMs in streamlining clinical trial data extraction. For example, [PERSON] et al. (2025) demonstrated that advanced models such as Claude-3.5 and Moonshot-v1 achieve approximately 95% accuracy in extracting data from complementary medicine trials, with accuracy surpassing 97% when supplemented with expert oversight [47]. Similarly, [PERSON] et al. (2025) showcased the effectiveness of ChatGPT-4o as a second reviewer in systematic reviews of exercise interventions, which achieved 92.4% accuracy with minimal false-positive outputs [48]. Furthermore, [PERSON] et al. (2025) utilized structured prompting methods that achieved 94.8% accuracy while significantly reducing extraction times to approximately 88 s per trial, which underscores the practical advantages of structured prompts in systematic review contexts [49].  
Despite these encouraging findings, the literature on LLM-driven clinical trial data extraction remains relatively underdeveloped, often limited to single-model evaluations or narrow clinical contexts. Multi-model evaluations have predominantly targeted clinical narratives and EHRs. These have achieved accuracy rates exceeding 98% with models such as GPT-4 and Claude variants [49]. [PERSON] et al. (2025) further confirmed the potential of LLMs in clinical documentation, notably enhancing medication data completeness from EHRs, though their study did not explicitly address clinical trial protocols [50]. Furthermore, [PERSON] et al. (2024) introduced a collaborative two-LLM workflow which demonstrated substantial improvements in accuracy and reliability over single-model approaches [51]. However, systematic benchmarking across multiple LLMs specifically targeting structured clinical trial data extraction remains sparse. Additionally, specialized domains such as aging research and neuromodulatory interventions have received minimal attention in the current literature. [PERSON] et al. (2024) notably highlighted a critical limitation of LLMs--low performance (approximately 39% accuracy) when extracting complex continuous outcome data--which underscores the ongoing challenges inherent in these specialized research areas [52].  
Thus, there is a significant gap in comprehensive, multi-LLM benchmarking studies tailored explicitly to nuanced and domain-specific clinical trial contexts. To date, no research has systematically evaluated multiple state-of-the-art LLMs specifically for extracting structured data from clinical trial documents involving aging populations or neuromodulatory techniques, such as brain stimulation. Furthermore, the accuracy of LLMs in handling continuous numeric outcomes and the benefits of cross-model consensus workflows has been virtually unexplored. The present study directly addresses these gaps by systematically benchmarking multiple LLMs for structured data extraction from aging-related tDCS and other neuromodulatory intervention trials.