Background: Large language models (LLMs) are increasingly used in medical and dental education to enhance clinical reasoning, patient communication, and academic learning. This study evaluates the effectiveness of four advanced LLMs— ChatGPT-4 (OpenAI), Claude 3.5 Sonnet (Anthropic), Microsoft Copilot, and Grok 3 (xAI)—in conveying fluoride-related dental knowledge. Methods: A cross-sectional comparative study was conducted using a mixed-methods approach. Each LLM answered 50 multiple- choice questions (MCQs) and 10 open-ended questions on fluoride chemistry, clinical applications, and safety concerns. Two blinded experts rated the open-ended responses on accuracy, depth, clarity, and evidence. Interrater reliability was assessed using Cohen’s kappa and Spearman’s correlation, and statistical analyses were performed using analysis of variance, Kruskal-Wallis, and post-hoc tests. Results: All models showed high MCQ accuracy (88%–94%). Claude 3.5 Sonnet achieved the highest scores in open-ended responses, especially for clarity (p=0.009). Minor differences in accuracy, depth, and evidence were not statistically significant. Overall, all LLMs performed strongly, with high interrater agreement supporting result reliability. Conclusion Advanced LLMs show strong potential as supportive tools in dental education and patient communication on fluoride use. Claude 3.5 Sonnet demonstrated superior linguistic clarity, enhancing its educational value. Continued evaluation and clinical oversight are crucial for their safe and effective integration into dentistry.

Objectives: The purpose of this retrospective epidemiological study was to determine the etiology and pattern of maxillofacial injuries in a pediatric population. Materials and Methods: Data for pediatric maxillofacial trauma patients aged 12 years and younger who were registered at the Department of Pediatric and Preventive Dentistry, Dr. R. Ahmed Dental College and Hospital, Kolkata, India, were reviewed and examined. Patients who were treated between October 2016 and September 2018 were analyzed according to age, sex, cause of injury, frequency and site of facial fractures, and soft tissue injuries. The chi-square tests were carried out for statistical analyses with a significance level of 5%. Results: Of 232 patients with a mean age of 6.77±3.25 years, there were 134 males (57.8%) and 98 females (42.2%). The overall male to female ratio was 1.39:1. The most common causes of injuries were falls (56.5%) and motor vehicle accidents (16.8%). Incidence of falls decreased significantly with age (P<0.001). Dentoalveolar injuries (61.6%) and soft tissue injuries (57.3%) were more common than facial fractures (42.7%). Mandibular fractures (82.8%) were the most common facial fractures, and perioral or lip injuries were the most prevalent injuries in our patient population. There was a positive association between facial fractures and soft tissue injury (P<0.01) (odds ratio 0.26; confidence interval 0.15-0.46). Conclusion Falls were the leading cause of maxillofacial trauma in our sample of children, and the most common site of fractures was the mandible.