Objective To investigate the association of serum albumin and relevant composite indicators with malignant brain edema after acute ischemic stroke. Methods We screened patients with acute ischemic stroke admitted to the Department of Neurology, West China Hospital of Sichuan University between January and December 2022. The case group consisted of patients who developed malignant brain edema within 7 days of admission, while the control group consisted of patients who did not develop malignant brain edema within 7 days of admission. Multivariate logistic regression analysis was used to explore the association of serum albumin and relevant composite indicators with malignant brain edema after acute ischemic stroke. Results Finally, 428 patients were included, aged 70.00 (58.00, 82.00) years, with females accounting for 40.9% (n=175). The time from onset to admission was 10.00 (4.00, 24.00) hours. 43 patients (10.0%) developed malignant brain edema and were classified as the case group. The onset time of malignant brain edema was 34.00 (22.50, 56.50) hours after the onset of the disease. Multivariate logistic regression analysis showed that the increase in the score of the baseline National Institutes of Health Stroke Scale scores [(odds ratio, OR)=1.167], the combination of diabetes (OR=5.525), the treatment of thrombectomy (OR=23.875), the neutrophil percentage-to-albumin ratio higher than the median (OR=3.806) were associated with the increased risk of malignant brain edema, and the successful reperfusion after thrombectomy (OR=0.120) was associated with the reduced risk of malignant brain edema (P<0.05). Conclusion A higher percentage of serum neutrophil percentage-to-albumin ratio within 24 hours of onset in patients with acute ischemic stroke is associated with an increased risk of malignant brain edema within 7 days of admission.
Large language models (LLMs), a key component of artificial intelligence (AI), represent a significant breakthrough in natural language processing. As the capabilities of LLMs continue to evolve, their potential applications and future implications in clinical medical education warrant considerable attention. This study systematically reviews the development of LLMs, explores their innovative applications within the context of current challenges in clinical medical education, and critically assesses both the advantages and limitations of their implementation. The objective is to provide a comprehensive reference for the continued integration of AI-driven LLMs into clinical medical education.