Large Language Models Versus Anesthesiologists for ASA Physical Status Classification

Adult patients who underwent preoperative anesthesia evaluation before elective surgery at Marmara University Pendik Training and Research Hospital will be included retrospectively. For each patient, demographic data (age, sex, body mass index), systemic comorbidities (hypertension, diabetes mellitus, coronary artery disease, chronic obstructive pulmonary disease, and others), functional capacity (metabolic equivalents, MET), type of planned surgery (major/minor), and the ASA-PS class assigned by the attending anesthesiologist will be recorded.

Clinical data will be anonymized and converted into structured clinical vignettes by the investigators. Vignettes will contain no identifiers, dates, protocol numbers, or rare diagnostic combinations that could directly or indirectly identify a patient.

Standardization of the LLM assessment process: To ensure independence between assessments, each vignette will be evaluated in a separate, history-free session. A new conversation will be initiated in the relevant model for every patient vignette, thereby eliminating the possibility that the model is influenced by its responses to previous vignettes (context anchoring). The ASA-PS class assigned to one vignette will not be carried over as context into the evaluation of any subsequent vignette. Each vignette will be presented to all four models using an identical, standardized prompt requesting only an ASA-PS class (I-VI) with a brief rationale, in a strictly defined output format. Model outputs will play no role in clinical decision-making. External information retrieval by the models will be disabled, and all queries will be completed within a narrow time window to minimize variability in model versions.

Each vignette will be submitted to each model once (single querying). Consequently, the intra-model test-retest reliability of the LLMs will not be assessed; this is acknowledged as a study limitation, consistent with the probabilistic nature of large language models, which may produce between-session variability in their outputs.

Model versions: The current version of each model available at the time of data collection will be used - ChatGPT (GPT-5.5, OpenAI), Gemini (Gemini 3.5, Google DeepMind), DeepSeek (DeepSeek V4, DeepSeek AI), and Claude (Claude Opus 4.8, Anthropic). These versions reflect the versions current at the time of protocol submission; the most recent stable version of each model accessible during data collection will be used, and the exact version and access date will be recorded. Because publicly available chat interfaces may perform automatic background routing to different model tiers, this is acknowledged as a reproducibility limitation.

The reference standard ASA-PS class will be determined by an independent, blinded panel of at least three senior anesthesiologists; consensus or majority vote will define the reference classification.

Statistical analysis: The primary (confirmatory) analysis will quantify the agreement between each LLM and the reference standard using quadratic weighted Cohen's kappa, respecting the ordinal structure of ASA-PS. Multi-rater agreement across the four models and the human raters will be assessed with Fleiss' kappa. Pairwise accuracy comparisons among the four models (six pairwise contrasts) will be treated as secondary/exploratory analyses and compared with McNemar or permutation tests for paired data, applying correction for multiple comparisons (e.g., Bonferroni or Holm); 95% confidence intervals will be estimated by bootstrap methods. Prespecified subgroup analyses include ASA III-IV boundary cases, multimorbidity burden, major versus minor surgery, and rater experience.

Primary hypothesis: The ASA-PS assignments of the LLMs (ChatGPT, DeepSeek, Gemini, and Claude) will show at least good agreement with the reference standard (weighted kappa ≥ 0.60). Secondary hypothesis: LLM errors will cluster in specific subgroups (e.g., the ASA III-IV boundary, multimorbid patients).

• Overall classification accuracy of each LLM [Time frame: Through study completion, an average of 3 months]
• Subgroup error patterns [Time frame: Through study completion, an average of 3 months]