This cross-sectional study evaluates the clinical accuracy, relevance, clarity, and emotional sensitivity of responses to inquiries from patients undergoing surgery provided by large language models (LLMs), highlighting their potential as adjunct tools in patient communication and education. Our findings demonstrated high performance of LLMs across accuracy, relevance, clarity, and emotional sensitivity, with Anthropic’s Claude 2 outperforming OpenAI’s ChatGPT and Google’s Bard, suggesting LLMs’ potential to serve as complementary tools for enhanced information delivery and patient-surgeon interaction.

We reviewed our experience using transulnar access (TUA) to obtain intraoperative cerebral angiography during prone surgery for vascular pathology, where conventional transfemoral and transradial access can be difficult. Ten consecutive patients treated between April 2020 and August 2025 were included. Ulnar artery access was obtained in the supine position before the patient was turned prone for surgery, and angiography was performed after the procedure without repositioning. Eight patients had arteriovenous malformations and 2 had dural arteriovenous fistulas. In all cases, intraoperative angiography was successfully completed through the ulnar artery. The mean ulnar artery diameter was 2.4 mm, indicating adequate vessel size for catheterization, and mean fluoroscopy time was 7.5 minutes. No immediate access-site complications occurred, and no case required conversion to another access route. These findings suggest that TUA is technically feasible and may provide a practical option for intraoperative cerebral angiography when prone positioning limits access to traditional arterial sites. Although the study is limited by its small sample size and retrospective design, the consistent procedural success supports further investigation.

A quantification technique for costal cartilage calcification using ¹⁸F-sodium fluoride–positron emission tomography/computed tomography (¹⁸F-NaF-PET/CT) has yet to be established, and the effects of aging and other demographic variables on costal cartilage calcification remain understudied. This study aims to introduce a quantification methodology for assessing costal cartilage calcification using ¹⁸F-NaF-PET/CT, assess age-related changes in its ¹⁸F-NaF uptake in females and males, and examine the relationship between its ¹⁸F-NaF uptake and CT attenuation as well as ¹⁸F-NaF uptake and coronary artery calcification. In this retrospective study, we analyzed subjects from the Cardiovascular Molecular Calcification Assessed by ¹⁸F-NaF PET/CT (CAMONA) clinical trial. This study evaluated 130 subjects (mean age 48.7 ± 14.5 years; n = 67 females). We manually generated regions of interest overlying the costal cartilages from ribs 8 to 10 on the left side, carefully avoiding osseous uptake from adjacent ribs and sternum, to measure cartilaginous ¹⁸F-NaF uptake. Non-parametric statistical analyses (Spearman correlations, Mann–Whitney U tests, Kruskal–Wallis tests) and receiver operating characteristic analysis were performed to evaluate sex-specific age-related changes in uptake, correlations between imaging parameters, and associations with coronary artery calcium (CAC) score. In females, the mean ¹⁸F-NaF uptake (as assessed by average SUV_mean) was 0.69 ± 0.38 while the corresponding mean Hounsfield Unit (HU) was 108.0 ± 40.0. In males, the mean ¹⁸F-NaF uptake (as assessed by average SUV_mean) was 0.63 ± 0.22, and the mean HU was 104.0 ± 24.0. There was a significant correlation between ¹⁸F-NaF uptake and age in both females (p = 0.003, r = 0.36) and males (p < 0.0001, r = 0.63). The correlation was significantly stronger in males than females (Fisher’s z-test, p = 0.040). There was a significant correlation between CAC score and costal cartilage SUV_mean in both females (r = 0.26, p = 0.036) and males (r = 0.51, p < 0.0001). This study introduces a quantification technique to assess costal cartilage calcification using ¹⁸F-NaF-PET/CT and demonstrates that the calcification increases with age, more strongly in males than in females, and ¹⁸F-NaF uptake is correlated with CAC score. This technique can be applied to other cartilages of interest, in both physiological and pathological conditions, to assess the effects of aging and various demographic variables on cartilage calcification.

Early re-operation secondary to postoperative surgical site infection (SSI) in lumbar microdiscectomy, although rare, poses significant morbidity and healthcare burden. Given the variable performance of traditional statistical and machine learning (ML) models in predicting this outcome, this study aimed to develop and validate a ML model to accurately predict 30-day re-operation following SSI in patients undergoing lumbar microdiscectomy. De-identified patient data were obtained from American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), focusing on patients with microdiscectomy. After applying inclusion criteria, 79,870 eligible cases were analyzed, of which 462 (0.6%) experienced re-operation following SSI. A nested cross-validation pipeline with Bayesian optimization was implemented using extreme gradient boosting (XGBoost) combined with Synthetic Minority Over-sampling Technique (SMOTE), and multiple classification thresholds were evaluated. Model interpretability was assessed through SHapley Additive Explanations (SHAP) to identify the most influential predictors. The final model achieved an area under the receiver operating characteristic curve of 0.996, with accuracy consistently at 0.993 and sensitivity of 0.924-0.985 across thresholds. Specificity remained at 0.993, while the positive predictive value fluctuated modestly (0.436–0.437), and the negative predictive value was 1.000. SHAP analysis highlighted any SSI, race, smoking status, diabetic status, and revised Risk Analysis Index scores as top predictors influencing re-operation risk. An optimized ML approach incorporating synthetic data augmentation yielded high predictive performance for an infrequent yet critical complication of lumbar microdiscectomy. These findings underscore the potential of data-driven models to enhance perioperative risk stratification and support targeted preventive strategies.

According to a recent study, integrating amyloid-PET, structural MRI, carotid Doppler ultrasound, cognitive testing, and APOE genotyping improves estimation of dementia progression in patients with mild cognitive impairment, with carotid plaque burden emerging as a dominant predictor in amyloid-β–negative cases. We agree that vascular pathology is crucial but suggest that future models consider supplementing amyloid-PET with FDG-PET, a well-validated functional marker of neuronal injury, and carotid Doppler with ¹⁸ F-sodium fluoride (NaF)-PET which can detect and quantify early, active microcalcification. This could further improve risk stratification while facilitating detection of potentially modifiable disease processes.