## 5 Discussion  
The primary findings of this study demonstrate that individual large language models (LLMs) exhibit distinct strengths and weaknesses when extracting key parameters from clinical trial protocols involving transcranial direct current stimulation (tDCS) in aging research [57; 58; 59; 60; 61]. Consistent with previous research [11; 12; 62], all tested models reliably identified fundamental binary attributes, such as the presence or absence of brain stimulation and whether interventions were invasive or noninvasive, achieving near-perfect to substantial agreement. However, considerable variability was observed in extracting more nuanced or numeric protocol features, such as secondary anatomical targets or session durations, reflecting inherent differences in model architectures and training strategies [63; 64]. The observed differences in performance across large language models (LLMs) likely stem from their underlying architectures, parameter sizes, and training corpora. Larger, instruction-tuned models such as Meta Llama 3.3-70B and DeepSeek-Distill 70B consistently performed better in extracting complex numeric details and nuanced descriptions due to their extensive training on large, domain-specific datasets, which likely provide them with richer contextual embeddings. In contrast, smaller, general-purpose models (e.g., openai/o1-mini, Google Gemini-Flash-1.5-8B) struggled with ambiguous language or imprecise numeric references, likely because they rely heavily on general linguistic patterns without sufficient domain-specific exposure. Additionally, the moderate agreement seen in identifying anatomical targets appears rooted in protocol ambiguity, where indirect references (such as \"regions associated with cognitive function\") create inherent interpre  
Figure 3: Pairwise comparison analysis for “Brain Stimulation Used”: (**a**) Exact match agreement Matrix, which shows the percentage of trials where models give identical responses. (**b**) [PERSON]’s kappa scores between each pair of LLMs, which shows agreement adjusted for chance. (**c**) Confusion matrices for selected model pairs, in which each matrix shows how often one model classifies a trial as “Yes” or “No” given the other model’s classification. tative challenges. The strength of ensemble methods observed here can be attributed to their ability to compensate for individual model biases and inconsistencies, highlighting their advantage in reducing errors through consensus. Finally, persistent variability and occasional inaccuracies among all models underscore inherent limitations in current LLM technology, emphasizing the critical role of continued human oversight, particularly for ensuring reliability in complex, clinically nuanced data extraction. As demonstrated in Table 6, majority voting and consensus strategies effectively distilled individual LLM outputs into a coherent record, facilitating rapid identification and appraisal of relevant trial data. Furthermore, the consistent patterns observed in Table 7, where inter-model agreement varied by field complexity, underscore the importance of careful curation and targeted prompt engineering to improve extraction accuracy, specially Figure 4, for complex protocol elements. The \(\kappa\) and ICC scores reported here translate directly into practical guidance for implementing automated extraction pipelines in systematic reviews. High agreement metrics affirm the capability of multi-LLM ensembles to substantially streamline the extraction of clearly defined protocol details. However, lower reliability observed for ambiguous or sparsely documented attributes underscores the continued necessity of structured human oversight. Thus, researchers should leverage these findings to strategically integrate human validation points, especially for nuanced protocol elements. This would ensure the robust applicability of LLM-based systems in real-world settings.