## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Field** & **FleissKappa** & **Mean \%** & **\# Trials** & **Categories** \\ \hline  & &  
Table 7 (Per-field agreement vs. majority) presents how often each LLM's output matches the consensus label for each field. Binary fields like \"Brain Stim.\" and \"Is Noninvasive\" achieve \(\geq\)90% agreement in nearly all cases, with some models (x.ai Grok and DeepSeek Distill 70B) reaching 100%. Numeric fields such as \"Intensity\" and \"Duration\" show lower alignment (68-94%), reflecting inconsistencies in how dosage or session length is described. \"Primary Target\" also remains moderately challenging: while most models exceed 75%, DeepSeek Distill 70B dips near 50%, possibly indicating heightened sensitivity to ambiguous text or multiple target mentions within a single protocol. Overall, these results confirm that the multi-agent LLM pipeline reliably classifies fundamental trial features, though textual ambiguity still introduces moderate variation, especially in multi-arm or highly detailed protocols. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Field** & **FleissKappa** & **Mean \% Agreement** & **\# Trials** & **Categories** \\ \hline  & & & & 1 h/week, 1 h/week for 10 weeks, 1 min, 1 min, 1 min \\  & & & on, 5 s off, 10 sessions over 2 weeks, 13 min, 13 min, 1 min with 5 s breaks, 2 weeks, 20 min, 20 to 60 min, 20–30 min, 10 days, and 2 weeks, 3 sessions per week for 2–4 weeks, 3 sessions per week for 3 weeks (9 sessions total), 3 sessions per week, 6000 pulses each, for 2–4 weeks, 30 min, 30 min for 5 successive days for temporal cortex stimulation, 30 min per session, 30 min per session, 10 sessions over two weeks, 4 weeks, 4-week (3 times per week) treatment, 4000 pulses each, every 6 months for an average of two to four years; 3 pts sessions (6000 pulses each) per week for 2–4 weeks, 5 consecutive days, 5 consecutive days, 30 min per session, 5 sessions/day over 4 days, 5 sessions/day over 4 days, 6 sessions over 2 weeks, 8 sessions of anodal tdcs completed twice a week for 4 weeks, 8 sessions, twice a week for 4 weeks, 9 sessions (3 sessions per week for 3 weeks), acute (one-time), applied during training sessions, approximately 20 min, daily over 4 weeks, during slow wave sleep, missing, null, repeated sessions, two weeks, two-week, varied (e.g., 3 times per week for 2–4 weeks) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**(a)**} \\ \hline
**Field** & **FleissKappa** & **95\% CI** & **Interpretation** \\ \hline brain\_stimulation\_used & 0.904 & 0.68–1.0 & Almost Perfect \\ primary\_type & 0.709 & 0.6–0.82 & Substantial \\ is\_noninvasive & 0.59 & 0.4–0.71 & Moderate \\ \hline  & \multicolumn{3}{c}{**(b)**} \\ \hline
**Parameter** & **ICC(2,1)** & **95\% CI** & **Interpretation** \\ \hline intensity & 0.95 & 0.9–0.98 & Excellent \\ duration & 0.964 & 0.94–0.98 & Excellent \\ frequency & 1 & 1.0–1.0 & Excellent \\ \hline \hline \end{tabular} (a) Fleiss’ kappa for categorical fields (bootstrap CI): each categorical field was converted into a contingency matrix (N\(\times\)k), and Fleiss’ \(\kappa\) was computed using a naive bootstrap (1000 resamples) to derive approximate 95% confidence intervals. (b) ICC for numeric fields (Pingouin, ICC2): Numeric fields were parsed as floats (e.g., “140 Hz” \(\rightarrow\) 140.0) and processed via pingouin’s intraclass\_corr to obtain the ICC(2) (two-way random, single measure). We report the ICC estimate, 95% CI, and a qualitative interpretation (Excellent, Good, Moderate, or Poor).  
\end{table} 
Table 5: Inter-model reliability and agreement statistics. Figure 3 provides detailed pairwise comparisons among models for the binary field 'Brain Stimulation Used'. The high [PERSON]'s kappa values (\(\geq\)0.90) indicate excellent inter-model reliability, further validating the robustness of the LLM-based extraction process.