OBJECTIVE: This paper presents the results from a competition challenging participants to develop entity linking models using a subset of annotated MIMIC-IV-Note data and the SNOMED CT Terminology. MATERIALS AND METHODS: As a basis for this work, a large set of 74 808 annotations was curated across 272 discharge notes spanning 6624 unique clinical concepts. Submissions were evaluated using the mean Intersection-over-Union metric, evaluated at the character level with the 3 best performing solutions awarded a cash prize. RESULTS: The winning solutions employed contrasting approaches: a dictionary-based method, an encoder-based method, and a decoder-based method. DISCUSSION: Our analysis reveals that concept frequency in training data significantly impacts model performance, with rare concepts proving particularly challenging. High concept entropy and annotation ambiguity were also associated with decreased performance. CONCLUSION: Findings from this work suggest that future projects should focus on improving entity linking for rare concepts and developing methods to better leverage contextual information when training examples are scarce.