A study examining large language model performance in hospital adverse-event reports found that a model exactly matched adjudicated expert severity labels in 49% of 286 reports. While the system showed strong ordinal agreement, discordances skewed toward under-classification, underscoring safety and calibration concerns for real-world clinical documentation use.