## 4 Results  
The multi-model pipeline analyzed a total of 83 aging-related clinical trials that potentially involved noninvasive brain stimulation, yielding systematically extracted data from five large language models (LLMs). To contextualize the consensus across models, Table 2 (Brain Stimulation Used) illustrates the first 10 trials' results. Here, each row indicates how many models returned a \"Yes\" or \"No\" for the presence of brain stimulation, along with the final consensus and the number of discrepant models. Notably, all trials except one (NCT05511259) elicited unanimous agreement, underlining the relative clarity of references to stimulation methods in protocol descriptions. Moving on to Table 3 (primary type, intensity, and confidence), we include each LLM's parsing of the critical attributes--primary type, intensity, and confidence for the first five unique NCT identifiers. In most instances, the models converge on \"tDCS,\" though some records incorporate variations (e.g., \"ctDCS,\" \"TMS\"). The intensity column often remains blank, or partially populated (e.g., \"2 mA\")when the text provides a clear numeric detail. Confidence levels here trend strongly toward \"High\" (Figure 2) but the variability across models becomes more evident as additional trials are analyzed. Table 2 presents the two sample trials (NCT05511259 and NCT06501755) where the five models did not unanimously vote \"Yes\" or \"No\". \begin{table}
\begin{tabular}{c c c c} \hline \hline
**NCTId** & **Model** & **Primary Type** & **Intensity** & **Confidence** \\ \hline NCT06658795 & OpenAI\_o1\_mini & tDCS & None & High \\ NCT06658795 & x\_ai\_grok\_2\_1212 & tDCS & None & High \\ NCT06658795 & Meta\_LLaMA\_3\_70B & tDCS & None & High \\ NCT06658795 & Google\_Gemini\_Flash\_1\_5\_8b & tDCS & None & High \\ NCT06658795 & DeepSeek\_Distill\_70b & tDCS & None & High \\ NCT03814304 & OpenAI\_o1\_mini & tDCS & None & High \\ NCT03814304 & x\_ai\_grok\_2\_1212 & tDCS & None & High \\ NCT03814304 & Meta\_LLaMA\_3\_3\_70B & tDCS & None & High \\ NCT03814304 & Google\_Gemini\_Flash\_1\_5\_8b & tDCS & None & High \\ NCT03814304 & DeepSeek\_Distill\_70b & tDCS & 2 mA & High \\ NCT02436915 & OpenAI\_o1\_mini & tDCS & None & High \\ NCT02436915 & x\_ai\_grok\_2\_1212 & tDCS & None & High \\ NCT02436915 & Meta\_LLaMA\_3\_3\_70B & tDCS & None & High \\ NCT02436915 & Google\_Gemini\_Flash\_1\_5\_8b & tDCS & None & High \\ NCT02436915 & DeepSeek\_Distill\_70b & tDCS & 2 mA & High \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Primary type, intensity, and confidence. Figure 2: Distribution of confidence ratings across five large language models (LLMs). Results demonstrate a strong predominance of “High” confidence ratings, with minor variations in the frequency of “Medium” or “Low” ratings. Notably, GPT-o1-mini occasionally assigns “Medium” or “Low” confidence, whereas DeepSeek Distill 70B consistently maintains “High” confidence”. Table 3 presents each model's extracted Primary Type of brain stimulation, its intensity (e.g., 2 mA), and a confidence rating for the first five unique NCT IDs. It demonstrates how different large language models parse complex clinical trial descriptions.