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.

BACKGROUND

The 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.

OBJECTIVE

This 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.

METHODS

We 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, F1-scores, and Cohen κ. To assess interrater reliability and establish a human benchmark, a stratified 10% (n=65) subset was independently annotated by a second clinician, and Cohen κ was calculated between annotators and between each model and the primary expert.

RESULTS

Interrater 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 F1-scores of 0.76-0.78 and macroaveraged F1-scores of 0.50-0.63. For binary classification, accuracies ranged from 0.93 to 0.94, with weighted F1-scores of 0.93-0.95 and Cohen κ of 0.76-0.79, approaching the human interrater benchmark. On a balanced 50-case subset, used as the sole basis for direct cross-model comparison, proprietary models achieved accuracies of 0.78 for fine-grained and 0.94-0.98 for binary classification. An ensemble approach yielded additional gains in classification performance.

CONCLUSIONS

LLMs 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.