ChatGPT as an information tool in rhinology. Can we trust each other today?

PURPOSE: ChatGPT (Chat-Generative Pre-trained Transformer) has proven to be a powerful information tool on various topics, including healthcare. This system is based on information obtained on the Internet, but this information is not always reliable. Currently, few studies analyze the validity of these responses in rhinology. Our work aims to assess the quality and reliability of the information provided by AI regarding the main rhinological pathologies. METHODS: We asked to the default ChatGPT version (GPT-3.5) 65 questions about the most prevalent pathologies in rhinology. The focus was learning about the causes, risk factors, treatments, prognosis, and outcomes. We use the Discern questionnaire and a hexagonal radar schema to evaluate the quality of the information. We use Fleiss's kappa statistical analysis to determine the consistency of agreement between different observers. RESULTS: The overall evaluation of the Discern questionnaire resulted in a score of 4.05 (± 0.6). The results in the Reliability section are worse, with an average score of 3.18. (± 1.77). This score is affected by the responses to questions about the source of the information provided. The average score for the Quality section was 3.59 (± 1.18). Fleiss's Kappa shows substantial agreement, with a K of 0.69 (p < 0.001). CONCLUSION: The ChatGPT answers are accurate and reliable. It generates a simple and understandable description of the pathology for the patient's benefit. Our team considers that ChatGPT could be a useful tool to provide information under prior supervision by a health professional.

Two-stage melting of Au-Pd nanoparticles.

Several series of molecular dynamics runs were performed to simulate the melting transition of bimetallic cuboctahedral nanoparticles of gold-palladium at different relative concentrations to study their structural properties before, in, and after the transition. The simulations were made in the canonical ensemble, each series covering a range of temperatures from 300 to 980 K, using the Rafii-Tabar version of the Sutton and Chen interatomic potential for metallic alloys. We found that the melting transition temperature has a strong dependence on the relative concentrations of the atomic species. We also found that, previous to the melting transition, the outer layer of the nanoparticle gets disordered in what can be thought as a premelting stage, where Au atoms near the surface migrate to the surface and remain there after the particle melts as a whole. The melting of the surface below Tm is consistent with studies of the interaction of a TEM electron beam with Au and Au-Pd nanoparticles.