Background: The American Society of Anesthesiologists Physical Status (ASA-PS) classification is widely used for perioperative communication and research with known variation in agreement amongst assessors. Large language models (LLM) are increasingly considered for uniform decision making due to agreement challenges within clinicians under identical inputs. The study compared four contemporary LLMs against clinician-derived consensus and quantified within-model stability across repeated assessments. Methods: In a cross-sectional vignette study, 228 anesthesiologists from Denmark, England, and India classified 20 standardized perioperative vignettes through online survey. The modal response per vignette was considered as clinician consensus. Four LLMs (ChatGPT-5.2 Plus, Gemini 3 Pro, Perplexity AI Pro, Claude 4 Sonnet) received same set of vignettes through identical prompts. Temporal stability was assessed by repeating each vignette query nine times per model (three-time windows across three nonconsecutive days) in fresh sessions. Primary outcome was exact agreement with clinician consensus. Results: Consensus agreement for modal LLM classifications was 18/20 (90%) for ChatGPT, 17/20 (85%) for Gemini, 17/20 (85%) for Claude, and 15/20 (75%) for Perplexity. Disagreement is clustered in vignettes with weak or split clinician consensus. Stability differed by model: Claude was fully stable across all vignettes (20/20), Gemini 19/20, ChatGPT 18/20, and Perplexity 14/20; instability typically involved adjacent-class shifts. Conclusions: Contemporary LLMs often match clinician modal judgement and are largely temporally stable, with discordance concentrated in clinically ambiguous boundary cases.

Background and Objectives: Breast cancer surgeries offer challenges in perioperative pain management, especially in the presence of inherent risk of postoperative nausea and vomiting (PONV) and postmastectomy pain syndrome (PMPS). Inappropriate opioid consumption was speculated as one of the reasons. Through this study, the influence of objective pain monitoring through a nociception level monitor (NOL) on perioperative course in breast surgeries was investigated. Materials and Methods: This was a prospective randomized study conducted at a regional hospital. Sixty female patients posted for breast cancer surgery were randomized equally into study and control groups. Both groups were monitored using BIS and NOL, but in the control group, the NOL monitor was blinded by a cover. Both groups received propofol and remifentanil through target-controlled infusions (TCIs) along with interpectoral, pectoserratus (PECS II), and superficial pectointercostal block. The primary outcome was intraoperative opioid consumption. Secondary outcomes were PONV, eligibility for discharge from the recovery room, and symptoms of PMPS after three months. Results: Two patients were excluded. The study group received significantly less remifentanil (0.9 mg in the study group vs. 1.35 mg in the control group, p = 0.033) and morphine (2.5 mg in study group vs. 5 mg in control group, p = 0.013). There was no difference in PMPS symptoms between the groups. The study group showed longer duration of inadequate analgesia (i.e., 7% vs. 10% of the total intraoperative period in control and study group, respectively, p = 0.008). There was no difference in time to eligibility for discharge from the recovery room between the groups. Conclusions: NOL monitor-guided analgesic delivery reduces intraoperative opioid consumption. No difference was demonstrated on PONV, eligibility for discharge from the recovery room, or PMPS symptoms.