\begin{table*}[hbt!]
    \centering
    \resizebox{0.9\linewidth}{!}{
    \begin{tabular}{lc|cccc}
        \textbf{Model Name} & \textbf{Type} & \textbf{Acc@1} & \textbf{Acc@3} & \textbf{Acc@5} & \textbf{Acc@100} \\
        \toprule
        RoBERT \cite{masala-etal-2020-robert} & \multirow{3}{*}{off-the-shelf} & 62.5 $\pm$ 0.005 & 76.8 $\pm$ 0.004 & 81.1 $\pm$ 0.005 & 92.0 $\pm$ 0.004\\
        BioClinicalBERT \cite{alsentzer2019clincalbert} &  & 66.7 $\pm$ 0.005 & 80.8 $\pm$ 0.003 & 84.6 $\pm$ 0.002 & 93.4 $\pm$ 0.002\\
        mE5 \cite{wangMultilingualE5Text2024} &  & 67.9 $\pm$ 0.004 & 85.2 $\pm$ 0.002 & 89.6 $\pm$ 0.002 & 98.1 $\pm$ 0.001\\
        \midrule
        RoBERT \cite{masala-etal-2020-robert} & \multirow{3}{*}{finetuned} & 84.4 $\pm$ 0.002 & 94.8 $\pm$ 0.002 & 96.6 $\pm$ 0.001 & 99.0 $\pm$ 0.001\\
        BioClinicalBERT \cite{alsentzer2019clincalbert} &  & 83.8 $\pm$ 0.003 & 94.3 $\pm$ 0.001 & 96.4 $\pm$ 0.001 & 99.0 $\pm$ 0.001\\
        mE5 \cite{wangMultilingualE5Text2024} &  & \textbf{85.2 $\pm$ 0.003} & \textbf{95.8 $\pm$ 0.001} & \textbf{97.5 $\pm$ 0.001} & \textbf{99.5 $\pm$ 0.001}\\
    \end{tabular}
    }
    \caption{Comparison between different types of text embedding models, having entries in the vector store from both the masterlist entries and associated clinical mappings. We obtained the best results using a fine-tuned version of mE5, a general-purpose multi-lingual model. Results are averaged across 5 folds.}
    \label{tab:historical}
\end{table*}