\begin{table*}[hbt!]
    \centering
    \resizebox{0.9\linewidth}{!}{
    \begin{tabular}{cc|cccc}
        \textbf{Vector Store Data} & \textbf{Index Type} & \textbf{Acc@1} & \textbf{Acc@3} & \textbf{Acc@5} & \textbf{Acc@100} \\
        \toprule
        \multirow{3}{*}{Masterlist Entries Only} 
        & sparse (BM25) & 52.6 $\pm$ 0.002 & 64.5 $\pm$ 0.002 & 68.5 $\pm$ 0.002 & 86.3 $\pm$ 0.001 \\
        & dense (mE5)    & 78.8 $\pm$ 0.002 & 92.2 $\pm$ 0.002 & 95.0 $\pm$ 0.002 & 99.5 $\pm$ 0.001 \\
        & hybrid (RRF)  & 63.9 $\pm$ 0.003 & 77.7 $\pm$ 0.003 & 82.1 $\pm$ 0.003 & 99.5 $\pm$ 0.001 \\
        \midrule
        \multirow{3}{*}{Masterlist Entries + Mappings} 
        & sparse (BM25) & 68.0 $\pm$ 0.003 & 82.3 $\pm$ 0.001 & 86.1 $\pm$ 0.001 & 94.7 $\pm$ 0.001 \\
        &  dense (mE5)   & \textbf{85.2 $\pm$ 0.003} & \textbf{95.8 $\pm$ 0.001} & \textbf{97.5 $\pm$ 0.001} & \textbf{99.5 $\pm$ 0.001} \\
        &  hybrid (RRF) & 81.0 $\pm$ 0.002 & 92.3 $\pm$ 0.001 & 94.9 $\pm$ 0.001 & 99.5 $\pm$ 0.000 \\
    \end{tabular}}
    \caption{Comparison between sparse embeddings from BM25, dense embeddings from mE5 \cite{wangMultilingualE5Text2024}, and hybrid search, having only masterlist entries in the vector store and having both masterlist and associated clinical mappings. Using dense embeddings from mE5 provides the best results in both cases. Results are averaged across 5 folds.}
    \label{tab:hybrid}
\end{table*}