There is a lack of benchmark data for trial-related tasks, with only a small portion of clinical trial records available for use. To encourage further research and compare different trial prediction method, it would be beneficial to create and release benchmark data. One notable exception is the TOP benchmark developed by Fu et al. , which focuses on predicting trial outcomes. This dataset comprises information about drugs, diseases, and eligibility criteria from a total of 17,538 clinical trials. The success rates vary across phases. Phase I (1,787 trials) has a success rate of 56.3%, phase II (6,102 trials) has 49.8%, while phase III (4,576 trials) has the highest success rate at 67.8%. HINT involved the integration of various sources of real-world data (RWD), such as drug compounds, disease ontology, and trial eligibility criteria. This integration was aimed at facilitating outcome predictions for trials across all phases. SPOT employed a meta-learning technique to organize trials with the same subject into a chronological sequence, leveraging insights from related trials and their predictive advancements. Other methods which are not specifically designed for clinical outcome prediction also are compared including Logistic regression (LR) , Random Forest (RF) , XGBoost , Adaptive boosting (AdaBoost) , k Nearest Neighbor (kNN) + RF and deep learning models, such as Feedforward Neural Network (FFNN) , DeepEnroll , COMPOSE . The latest results of the benchmark are shown in Table .