The Quality of Recovery-40 questionnaire (QoR-40) is a self-rated questionnaire used to assess the postoperative recovery quality and health status of patients in the early stages following surgery; however, there is no Turkish version of the QoR-40. The aim of this study was to assess the reliability, validity, and responsiveness of the Turkish version of the QoR-40 (QoR-40 T).

Despite varying according to the disease and the patient, emotional reactions such as anxiety, depression, regression, anger, and grieving responses are observed in the preoperative period. High preoperative anxiety levels can lead to increased need for postoperative pain relief, prolonged hospital stay, inadequate patient satisfaction and insufficient patient recovery.In the present study, it was aimed to measure preoperative anxiety and depression levels and to determine their relationship with quality of recovery in patients scheduled to undergo elective surgery under general anesthesia. A total of 137 patients older than 18 years of age who were planned for elective surgery and who were to be operated under general anesthesia were included in the study. In the preoperative period, the patients were asked to fill out the demographic information form, Beck anxiety inventory and Beck depression inventory. They were also asked to complete the quality of awakening and the quality of recovery questionnaires at the postoperative first day and first month.It was detected that the anxiety and depression scores were high, whereas the recovery quality was low in females. The depression scores of the patients who were college graduates were found to be low. It was observed that the quality of awakening and the quality of recovery values at the postoperative first day and first month were significantly lower in the patients with higher depression and anxiety scores. According to these results, reducing the anxiety and depression of the patients in the preoperative period to a minimum is necessary to minimize the negative effects in the postoperative period such as inadequate patient recovery, increased need for painkillers, long hospital stay and insufficient patient satisfaction.

In total knee arthroplasty (TKA), both surgical trauma and the type of anesthesia administered can significantly affect systemic inflammation, which may influence postoperative recovery. This retrospective study aimed to compare perioperative changes in blood-derived systemic inflammatory markers—specifically the systemic immune-inflammation index (SII) and the systemic inflammation response index (SIRI)—in patients undergoing TKA under general versus spinal anesthesia. This retrospective analysis included 849 patients who underwent elective primary TKA between January 2020 and April 2025. Inclusion criteria were age 18–75 years and ASA physical status I–II. Patients with BMI ≥ 40 kg/m², ASA ≥ III, major comorbidities, active infection, revision surgery, or incomplete lab data were excluded. Patients were categorized into spinal anesthesia (Group S) and general anesthesia (Group G) groups. Pre- and postoperative hemograms were used to calculate SII and SIRI values; ΔSII and ΔSIRI were defined as absolute differences. Groups were compared in terms of inflammatory markers, total opioid requirement, postoperative complications, and hospital stay. Group comparability regarding age, ASA, and comorbidities was assessed, and multivariate linear regression models were applied to evaluate predictors of ΔSII and ΔSIRI. A total of 849 patients were included in the study. The median ΔSII value was significantly higher in the general anesthesia group [Group G: 1448.47 (IQR: 677.78–2670.86)] compared to the spinal anesthesia group [Group S: 1060.75 (IQR: 463.69–2093.28); p < 0.001]. Similarly, the ΔSIRI value was higher in Group G [4.56 (IQR: 2.31–9.23)] than in Group S [3.69 (IQR: 1.70–7.05); p = 0.002]. The total opioid requirement within the first 24 postoperative hours was also significantly greater in Group G (p < 0.001). No statistically significant differences were found between the groups in terms of postoperative complication rates (p = 0.48) or length of hospital stay (p = 0.18). Compared to general anesthesia, spinal anesthesia was associated with a lower postoperative increase in systemic inflammatory blood markers in patients undergoing TKA. While no difference was observed in short-term clinical outcomes, these findings suggest that spinal anesthesia may offer an immunological advantage. Given the retrospective nature of the study, prospective research is warranted to determine whether these biomarker differences have meaningful clinical implications.

Background/Objectives: Large language models (LLMs) are increasingly used for patient education, yet comparative evidence regarding their accuracy, safety, and ethical performance remains limited, particularly in high-risk fields such as anesthesiology. This study aimed to conduct a multidimensional comparison of five contemporary LLMs in answering common patient questions in anesthesiology. Methods: In this cross-sectional, comparative in silico study, 30 standardized patient questions covering general anesthesia, spinal/epidural anesthesia, and peripheral nerve blocks were submitted to ChatGPT, Gemini, Microsoft Copilot, DeepSeek, and Grok. Responses were independently evaluated under full blinding by five senior anesthesiology professors using a 5-point Likert scale across six domains: accuracy, safety, completeness, understandability, ethics, and overall assessment. Inter-rater reliability was assessed using intraclass correlation coefficients (ICC). Performance differences were analyzed using linear mixed-effects models accounting for question- and evaluator-level variability, with results reported as estimated marginal means. Results: Inter-rater agreement was good to excellent across all domains (ICC > 0.75). Significant model-related differences were observed for overall assessment, accuracy, safety, completeness, and ethics (all p < 0.001), whereas understandability did not differ significantly between models. ChatGPT achieved the highest overall performance, while Gemini demonstrated superior accuracy. Model performance varied across anesthesiology subspecialties, with significant model × topic interactions identified in multiple domains (p < 0.01). Conclusions: LLMs may serve as supportive tools for patient education in anesthesiology; however, their performance varies substantially across models and clinical contexts. Differences in accuracy, safety, and ethical performance highlight the need for cautious, context-aware integration of LLMs into clinical practice rather than their use as substitutes for anesthesiologists’ clinical judgment.