Background: Allergic rhinitis (AR) impacts quality of life, work and school productivity. Over the last years, an important body of evidence resulting from mHealth data has led to a better understanding of AR. Such advances have motivated an EAACI‐endorsed update of the Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines (ARIA 2024–2025). This manuscript presents the ARIA 2024–2025 recommendations for intranasal treatments, one of the mainstays for AR management. Methods: The ARIA 2024–2025 guideline panel issued recommendations following the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) evidence‐to‐decision framework. Several sources of evidence were used to inform panel judgments and recommendations, including systematic reviews, evaluation of mHealth and pharmacovigilance data, as well as a survey of experts on costs. Results: Eleven guideline questions concerning intranasal treatments for AR were prioritized, leading to recommendations. Overall, these questions concern the choice between different classes of intranasal medications—most notably, intranasal corticosteroids (INCS), antihistamines (INAH), fixed combinations of INAH+INCS and decongestants—or between different individual medications within each class. Four questions had not been evaluated in previous ARIA guidelines, while for the other three there was a change in the strength or directionality of recommendations. Overall, recommendations point to the suggested use of INAH+INCS over INAH or INCS and INCS over INAH. Conclusion: This ARIA 2024–2025 article supports patients, their caregivers, and healthcare professionals in choosing an intranasal treatment. However, decisions on AR treatment should consider the clinical variability of the disease, patients' values, and the affordability of medications.

In recent months, there has been a new trend involving the consumption of Amanita muscaria. The aim of this article was to investigate the reasons for consumption, the form taken and the adverse symptoms that were indicated by those consuming Amanita muscaria. After analysing 5600 comments, 684 people were included in the study, who, in social media groups such as Facebook, stated the purpose of consuming the mushroom (n = 250), the form of mushroom they were taking (n = 198) or the adverse symptoms they experienced (n = 236). The gender of the subjects differentiated the parameters analysed. In the study group of women, the main purpose of consuming Amanita muscaria was to reduce pain, as well as to reduce skin problems, while in men it was mainly to relieve stress, reduce the severity of depressive symptoms and reduce insomnia (p < 0.001). With regard to the form of mushroom ingested, tincture was predominant in the women’s study group, while dried was predominant in the men (p < 0.001). In terms of side effects, women reported primarily headaches, while men reported nausea, vomiting, abdominal pain and drowsiness (p < 0.001). Advanced research on Amanita muscaria should be carried out to make the community aware of the toxicity of this fungus.

Background: Content generated by artificial intelligence is sometimes not truthful. To date, there have been a number of medical studies related to the validity of ChatGPT’s responses; however, there is a lack of studies addressing various aspects of statistical analysis. The aim of this study was to assess the validity of the answers provided by ChatGPT in relation to statistical analysis, as well as to identify recommendations to be implemented in the future in connection with the results obtained. Methods: The study was divided into four parts and was based on the exemplary medical field of allergology. The first part consisted of asking ChatGPT 30 different questions related to statistical analysis. The next five questions included a request for ChatGPT to perform the relevant statistical analyses, and another five requested ChatGPT to indicate which statistical test should be applied to articles accepted for publication in Allergy. The final part of the survey involved asking ChatGPT the same statistical question three times. Results: Out of the 40 general questions asked that related to broad statistical analysis, ChatGPT did not fully answer half of them. Assumptions necessary for the application of specific statistical tests were not included. ChatGPT also gave completely divergent answers to one question about which test should be used. Conclusion: The answers provided by ChatGPT to various statistical questions may give rise to the use of inappropriate statistical tests and, consequently, the subsequent misinterpretation of the research results obtained. Questions asked in this regard need to be framed more precisely.