Background/aim Frequent premature ventricular complexes (PVCs) have historically been regarded as benign in structurally normal hearts, yet emerging evidence suggests substantial cardiovascular risk. This meta-analysis aimed to quantify associations between frequent PVCs and incident atrial fibrillation, heart failure, stroke and all-cause mortality in adults without established cardiovascular disease. Methods PubMed/MEDLINE, Embase, CENTRAL, Web of Science and Scopus were searched through February 2025. Databases were searched from inception to February 2025. Eligible studies employed standardised PVC assessment methods with a minimum 12-month follow-up reporting adjusted effect estimates. Data were independently extracted and quality was assessed (Risk of Bias in Non-randomized Studies of Interventions) by two reviewers. Random-effects meta-analyses yielded pooled HRs with 95% CIs and prediction intervals (PI). Study-level meta-regression was used to evaluate dose–response relationships, and heterogeneity sources were explored via further meta-regression. Outcomes 20 articles (17 studies; 26 783 590 participants) were analysed. Frequent PVCs were significantly associated with increased risks of atrial fibrillation (HR 1.69, 95% CI 1.39 to 2.05; PI 0.91–3.12), heart failure (HR 1.73, 95% CI 1.50 to 2.00; PI 1.18–2.54), stroke (HR 1.28, 95% CI 1.10 to 1.50; PI 0.90–1.82) and all-cause mortality (HR 1.31, 95% CI 1.10 to 1.56; PI 0.79–2.18). Heterogeneity was substantially reduced in sensitivity analyses restricted to Holter-quantified PVCs. Meta-regression identified a 5.4% increased atrial fibrillation risk per 1% increment in PVC burden. Conclusion Frequent PVCs confer significantly increased cardiovascular risks in populations largely without overt structural heart disease, though baseline cardiac assessment varied across studies. Patients with frequent PVCs (≥500/day) may benefit from periodic echocardiography and rhythm monitoring to detect early structural or arrhythmic progression. Randomised trials are needed to determine if PVC burden-guided interventions can reduce cardiovascular risk. PROSPERO registration number CRD420251006111.

Background/aim: Large-scale language models (LLMs), such as GPT-4 and GPT-3.5, have demonstrated remarkable potential in the rapidly developing field of artificial intelligence (AI) in education. The use of these models in medical education, especially their effectiveness in situations such as the Turkish Medical Specialty Examination (TUS), is yet understudied. This study evaluates how well GPT-4 and GPT-3.5 respond to TUS questions, providing important insight into the real-world uses and difficulties of AI in medical education. Materials and methods: In the study, 1440 medical questions were examined using data from six Turkish Medical Specialties examinations. GPT-4 and GPT-3.5 AI models were utilized to provide answers, and IBM SPSS 26.0 software was used for data analysis. For advanced enquiries, correlation analysis and regression analysis were used. Results: GPT-4 demonstrated a better overall success rate (70.56%) than GPT-3.5 (40.17%) and physicians (38.14%) in this study examining the competency of GPT-4 and GPT-3.5 in answering questions from the Turkish Medical Specialization Exam (TUS). Notably, GPT-4 delivered more accurate answers and made fewer errors than GPT-3.5, yet the two models skipped about the same number of questions. Compared to physicians, GPT-4 produced more accurate answers and a better overall score. In terms of the number of accurate responses, GPT-3.5 performed slightly better than physicians. Between GPT-4 and GPT-3.5, GPT-4 and the doctors, and GPT-3.5 and the doctors, the success rates varied dramatically. Performance ratios differed across domains, with doctors outperforming AI in tests involving anatomy, whereas AI models performed best in tests involving pharmacology. Conclusions: In this study, GPT-4 and GPT-3.5 AI models showed superior performance in answering Turkish Medical Specialization Exam questions. Despite their abilities, these models demonstrated limitations in reasoning beyond given knowledge, particularly in anatomy. The study recommends adding AI support to medical education to enhance the critical interaction with these technologies.

Background. The relationship between heart failure (HF) and Mediterranean and DASH diets is not well delineated. This meta-analysis aimed to assess the effectiveness of high adherence to Mediterranean and DASH diets compared to low adherence in reducing the risk of incident HF (primary prevention of HF) and reducing all-cause mortality in patients with HF (secondary prevention of HF). Methods. The reporting stages of this meta-analysis closely adhered to the PRISMA guidelines. A comprehensive literature search was undertaken for published papers in PubMed, Embase, EBSCO, ICTRP, and the NIH clinical trials databases. Results. A total of 16 reports from 14 studies were included in this paper. A significant inverse association was identified between high adherence to the Mediterranean diet model (compared to low adherence) and the risk of incident HF (OR = 0.77, 95% CI: 0.63–0.93, p = 0.007) among patients without previous diagnosis of HF. Similarly, there was a significant and inverse relationship between high adherence to the DASH diet (compared to low adherence) and the risk of incident HF (OR = 0.83, 95% CI: 0.70–0.98, p = 0.03) among patients without previous diagnosis of HF. High adherence to the Mediterranean diet model (compared to low adherence) was associated with lower all-cause mortality (OR = 0.88, 95% CI: 0.78–0.99, p = 0.03) among patients with HF. Conclusions. This paper demonstrated that high adherence to Mediterranean and DASH diets significantly reduced the risk of incident HF among individuals without a previous diagnosis of HF, whereas only high adherence to the Mediterranean diet was associated with lower all-cause mortality among patients with HF.