The role of oral metformin in preventing and treating age-related macular degeneration: A meta-analysis.

BACKGROUND: We aimed to perform a meta-analysis to evaluate the effect of metformin on age-related macular degeneration. METHODS: We searched the following databases: PubMed, Scopus, and Web of Science. We included any randomized control trials, prospective and retrospective cohorts, cross-sectional studies, and case-control studies that investigated the effect of metformin on age-related macular degeneration in our meta-analysis with no age or language restrictions. Review manager software, version 5.4 was used to perform the meta-analysis. RESULTS: Ten studies were included in the meta-analysis with 1,447,470 patients included in the analysis. The pooled analysis showed no statistically significant difference between the metformin group and the non-metformin group regarding age-related macular degeneration (odds ratio [OR] = 0.37, confidence interval [CI] = (0.14-1.02), P = .05). Subgroup analysis showed no statistically significant difference between metformin group and non-metformin group regarding age-related macular degeneration in present or past metformin usage (OR = 0.19, CI = (0.03-1.1), P = .06), (OR = 0.61, CI = (0.25-1.45), P = .26), respectively, The pooled analysis showed no statistically significant difference between age-related macular degeneration group and control group regarding metformin usage (OR = 0.86, CI = (0.74-1.00), P = .05). The subgroup analysis showed no statistically significant difference between the age-related macular degeneration group and control group in <2 years of metformin usage and 2 years or more (OR = 0.89, CI = (0.52-1.52), P = .67), (OR = 0.95, CI = (0.82-1.10), P = .47), respectively. CONCLUSION: Our study revealed no role of metformin in decreasing age-related macular degeneration risk in past or present usage. More RCTs are needed to support our findings in evaluating the actual role of metformin in age-related macular degeneration.

AI chatbots show promise but limitations on UK medical exam questions: a comparative performance study.

Large language models (LLMs) like ChatGPT have potential applications in medical education such as helping students study for their licensing exams by discussing unclear questions with them. However, they require evaluation on these complex tasks. The purpose of this study was to evaluate how well publicly accessible LLMs performed on simulated UK medical board exam questions. 423 board-style questions from 9 UK exams (MRCS, MRCP, etc.) were answered by seven LLMs (ChatGPT-3.5, ChatGPT-4, Bard, Perplexity, Claude, Bing, Claude Instant). There were 406 multiple-choice, 13 true/false, and 4 "choose N" questions covering topics in surgery, pediatrics, and other disciplines. The accuracy of the output was graded. Statistics were used to analyze differences among LLMs. Leaked questions were excluded from the primary analysis. ChatGPT 4.0 scored (78.2%), Bing (67.2%), Claude (64.4%), and Claude Instant (62.9%). Perplexity scored the lowest (56.1%). Scores differed significantly between LLMs overall (p < 0.001) and in pairwise comparisons. All LLMs scored higher on multiple-choice vs true/false or "choose N" questions. LLMs demonstrated limitations in answering certain questions, indicating refinements needed before primary reliance in medical education. However, their expanding capabilities suggest a potential to improve training if thoughtfully implemented. Further research should explore specialty specific LLMs and optimal integration into medical curricula.

Efficacy and safety of TAVR versus SAVR in patients with small aortic annuli: A systematic review and meta-analysis.

INTRODUCTION: Patients with a small aortic annulus (SAA) undergoing aortic valve replacement are at increased risk of patient-prosthesis mismatch (PPM), which adversely affects outcomes. Transcatheter aortic valve replacement (TAVR) has shown promise in mitigating PPM compared to surgical aortic valve replacement (SAVR). METHODS: We conducted a systematic review and meta-analysis following PRISMA guidelines to compare clinical outcomes, mortality, and PPM between SAA patients undergoing TAVR and SAVR. Eligible studies were identified through comprehensive literature searches and assessed for quality and relevance. RESULTS: Nine studies with a total of 2476 patients were included. There was no significant difference in 30-day Mortality between TAVR vs SAVR groups (OR = 0.65, 95% CI [ 0.09-4.61], P = 0.22). There was no difference between both groups regarding myocardial infarction at 30 days (OR = 0.63, 95% CI [0.1-3.89], P = 0.62). TAVR was associated with a significantly lower 30-day major bleeding and 2-year major bleeding, Pooled studies were homogeneous (OR = 0.44, 95% CI [0.31-0.64], P < 0.01, I2 = 0, P = 0.89), (OR = 0.4 ,95% CI [0.21-0.77], P = 0.03, I2 = 0%, P = 0.62) respectively. TAVR was associated with a lower rate of moderate PPM (OR = 0.6, 95% CI [ 0.44-0.84], p value = 0.01, i2 = 0%, p value = 0.44). The overall effect estimate did not favor any of the two groups regarding short-term Mild AR (OR = 5.44, 95% CI [1.02-28.91], P = 0.05) and Moderate/severe AR (OR = 4.08, 95% CI [ 0.79-21.02], P = 0.08, I2 = 0%, P = 0.59). CONCLUSION: Our findings suggest that both TAVR and SAVR are viable options for treating AS in patients with a small aortic annulus. TAVR offers advantages in reducing PPM and major bleeding, while SAVR performs better in terms of pacemaker implantation. Future studies should focus on comparing newer generation TAVR techniques and devices with SAVR. Consideration of patient characteristics is crucial in selecting the optimal treatment approach for AS.