Aim: This study aimed to evaluate and compare the performance of 4 LLMs in responding to common prosthodontic complication scenarios. Methods: An expert panel design was employed to assess the quality of responses generated by the four LLMs across five clinically relevant prosthodontic scenarios. Each scenario was entered 15 times into each model, yielding 300 responses in total. 7 prosthodontists independently evaluated the anonymized responses based on four criteria: clinical accuracy, applicability, clarity, and comprehensiveness, using a 5-point Likert scale. Descriptive statistics, one-way ANOVA, and post-hoc pairwise comparisons were performed to detect inter-model differences. The internal consistency (reliability) of evaluators’ ratings for each model was assessed using Cronbach’s alpha. Results: Significant differences were found among the models for all evaluation criteria (p<.05). ChatGPT achieved the highest mean scores for clinical accuracy (3.89), clarity (3.90), and comprehensiveness (4.09), while Gemini showed the highest applicability (3.88). Cronbach’s alpha coefficients indicated strong reliability across all domains (0.776–0.880), with ChatGPT showing the highest single-domain coefficient (α=0.820–0.880). Conclusion: Among the evaluated models, ChatGPT and Gemini generally received higher ratings on specific criteria in pairwise comparisons; however, the four models should not be interpreted as forming a definitive overall ranking. While LLMs show potential as supportive tools in prosthodontic education and clinical decision-making, their outputs should not replace expert judgment.
Aim: This meta-analysis aimed to quantify differences in mortality and failure-to-rescue (FTR) between Magnet and non-Magnet hospitals. Methods: A systematic literature search was conducted for studies published between 2000 and 2018 using keywords related to magnet hospitals, non-magnet hospital, mortality, and FTR. Seven studies involving approximately 4.5 million patients met the inclusion criteria. Data were analyzed using the CMA software, with odds ratios (OR) calculated under a random-effects model. Heterogeneity was assessed using Cochran’s Q and I² statistics. Publication bias was evaluated using funnel plots and statistical analysis. Results: Compared with non-Magnet hospitals, Magnet hospitals showed 15% lower odds of mortality (OR = 0.85, 95% CI: 0.796–0.907, p <0.001) and 6% lower odds of FTR (OR = 0.94, 95% CI: 0.889–0.997, p = 0.038). Significant heterogeneity was observed, mainly attributed to differences in the data collection periods, with long-term studies demonstrating more pronounced effects. No publication bias was observed. Conclusion: Magnet hospital designation is associated with significantly reduced mortality and FTR rates, reflecting superior nursing environment and care quality.
Endometriosis is an estrogen-dependent chronic inflammatory disease that affects approximately 10% of reproductiveage women. Estrogen metabolism disorders, immune dysregulation, oxidative stress, and inflammation interact in the pathogenesis of these conditions. In recent years, the influence of gut microbiota on these processes has been extensively studied, particularly through the estrobolome mechanism and modulation of the immune response. Nutrition, as one of the important and modifiable factors shaping gut microbiota composition, has been reported to alleviate disease symptoms through its anti-inflammatory and antioxidant effects, mediated by components such as omega-3 fatty acids, antioxidant vitamins, polyphenols, and dietary fiber. This review addresses the interactions between the gut microbiota and nutrition through a holistic approach, evaluating the potential roles of specific nutrients and dietary patterns in reducing the risk of endometriosis and managing its symptoms in light of the current literature.
