Content quality control (QC) of clinical records is a critical task for ensuring patient safety. However, systematically assessing narrative admission notes for information completeness, diagnostic consistency, and logical coherence remains a significant challenge, and public benchmarks are lacking. To address this need, we organized the Content Quality Control Task for Admission Records in Inpatient Electronic Medical Records. This task aims to evaluate the deep reasoning capabilities of Large Language Models (LLMs) on Electronic Medical Record (EMR) samples. The evaluation was structured into two distinct tracks: a No-Finetuning Track (Track A) and a Finetuning Track (Track B). The shared task attracted 283 teams from industry and academia, with the top-performing teams achieving overall scores of 0.7023 (Track A) and 0.7311 (Track B). This paper provides a comprehensive overview of the task design, the datasets, the evaluation metrics, and a summary of the innovative methods employed by the top-ranking teams.