Guideline‐augmented prompting improves comparative preference and response consistency of large language model outputs for orthopaedic anaesthesia questions: A controlled prompting study

Abstract Purpose Large language models (LLMs) are increasingly used in clinical contexts; however, performance in complex perioperative decision‐making remains uncertain. Orthopaedic anaesthesia presents a demanding test case due to comorbidity burden and guideline‐dependent management. Whether successive LLM generations and guideline‐augmented prompting improve clinical alignment, comparative performance and response consistency was evaluated in this study. Methods In this controlled prompting study, 34 orthopaedic anaesthesia questions spanning seven clinical subdomains were independently answered by three LLMs (GPT‐3.5‐turbo, GPT‐4o and GPT‐5.2) and three expert anesthesiologists. GPT‐4o and GPT‐5.2 each generated responses with and without access to relevant clinical practice guidelines. All responses were anonymized and evaluated in blinded pairwise comparisons by an independent guideline‐informed LLM‐as‐a‐judge (GPT‐5.2) using the same guideline material as the reference standard. The judge recorded preference, response consistency and confidence. Intra‐model consistency was assessed from repeated independent generations of each question. Results All LLMs were preferred over human experts in pairwise comparisons without guideline augmentation, with win rates of 67.6% (GPT‐3.5‐turbo), 79.4% (GPT‐4o) and 95.1% (GPT‐5.2). Performance was improved by guideline augmentation, most notably for GPT‐4o (91.2%, +11.8 percentage points), while GPT‐5.2 approached ceiling performance (97.1%). Response consistency varied between models. GPT‐4o showed the highest baseline consistency (80.4%), whereas GPT‐5.2 demonstrated no fully contradictory outputs but showed greater partial variability. Guideline augmentation numerically improved GPT‐5.2 consistency (66.7% to 80.4%) and reduced inter‐model differences. Directed qualitative analysis suggested that reviewer preference for later GPT generations was associated with greater completeness, explicit clinical reasoning and guideline‐oriented responses. Conclusion Successive LLM generations demonstrated progressively improved performance in orthopaedic anaesthesia reasoning. Guideline‐augmented prompting enhanced response quality, particularly for intermediate models. Guideline‐informed LLM‐as‐a‐judge evaluation appears promising for comparative assessment but requires further validation. Level of Evidence NA.