Zero-Shot Classification of Postoperative Complications From Real-World Discharge Letters According to the Clavien-Dindo System Using Large Language Models in Liver Surgery: Comparative Study.

backgroundThe standardized extraction of postoperative complications from unstructured routine clinical documentation remains a major unresolved challenge in digital surgery and health informatics. Although the Clavien-Dindo classification is the established standard for grading postoperative complications, its application in routine clinical documentation is largely implicit and unstructured, limiting scalable quality assessment in surgical care.

objectiveThis study aimed to assess the capability of open-weight and proprietary large language models (LLMs) to classify postoperative complications according to the Clavien-Dindo system using discharge letters, benchmarked against expert annotation.

methodsWe analyzed discharge letters from 650 surgical cases of 649 patients (median 67, IQR 58-73 y; 229/649, 35% female) who underwent hepatobiliary surgery between 2010 and 2024. The cohort included grade I-II complications in 24% (153/650), grade III-IV in 19% (121/650), and grade V (death) in 6% (42/650) of patients. A total of 4 open-weight (Qwen3-235B [Alibaba Cloud], Llama-3.3-70B [Meta AI], GPT-OSS-120B [OpenAI], Ministral-3-8B [Mistral AI]) and 2 proprietary (GPT 5.1 [OpenAI], Gemini 3 Pro [Google]) LLMs were prompted to infer complication grades directly from the discharge letters in a zero-shot setting. Model performance was evaluated against expert assessment using accuracy, F

resultsInterrater agreement between the 2 clinical annotators yielded a Cohen κ of 0.75, providing a human benchmark for model performance interpretation. On the full 650-case dataset, open-weight models achieved accuracies ranging from 0.75 to 0.78 for fine-grained prediction, with weighted F

conclusionsLLMs demonstrated promising accuracy in classifying postoperative complications from discharge letters in a zero-shot setting, with performance approaching the upper bound of human interrater agreement. Open-weight models offer a particularly attractive trade-off between accuracy and computational efficiency, while ensemble strategies further enhance robustness. These results support the potential of LLMs to standardize complication assessment at scale and enable data-driven quality monitoring in surgical care.