Obstructive sleep apnoea/hypopnoea syndrome: Relationship with obesity and management in obese patients

SUMMARY Obstructive sleep apnoea/hypopnoea syndrome (OSAHS) is a disease characterised by upper airway obstruction during sleep, quite frequent in the general population, even if underestimated. Snoring, sleep apnoea and diurnal hypersomnia are common in these patients. Central obesity plays a key role: it reduces the size and changes the conformation of the upper airways, besides preventing lung expansion, with consequent reduction of lung volumes. Furthermore, obese people are also resistant to leptin, which physiologically stimulates ventilation; as a result, this causes scarce awakening during apnoea. OSAHS diagnosis is based on the combination of clinical parameters, such as apnoea/hypopnoea index (AHI), medical history, physical examination and Mallampati score. The first objective reference method to identify OSAHS is polysomnography followed by sleep endoscopy. Therapy provides in the first instance reduction of body weight, followed by continuous positive airway pressure (CPAP), which still remains the treatment of choice in most patients, mandibular advancement devices (MAD) and finally otolaryngology or maxillofacial surgery. Among surgical techniques, central is barbed reposition pharyngoplasty (BRP), used in the field of multilevel surgery

Nandrolone decanoate: Use, abuse and side effects

Background and Objectives: Androgens play a significant role in the development of male reproductive organs. The clinical use of synthetic testosterone derivatives, such as nandrolone, is focused on maximizing the anabolic effects and minimizing the androgenic ones. Class II anabolic androgenic steroids (AAS), including nandrolone, are rapidly becoming a widespread group of drugs used both clinically and illicitly. The illicit use of AAS is diffused among adolescent and bodybuilders because of their anabolic proprieties and their capacity to increase tolerance to exercise. This systematic review aims to focus on side effects related to illicit AAS abuse, evaluating the scientific literature in order to underline the most frequent side effects on AAS abusers’ bodies. Materials and Methods: A systematic review of the scientific literature was performed using the PubMed database and the keywords “nandrolone decanoate”. The inclusion criteria for articles or abstracts were English language and the presence of the following words: “abuse” or “adverse effects”. After applying the exclusion and inclusion criteria, from a total of 766 articles, only 148 were considered eligible for the study. Results: The most reported adverse effects (found in more than 5% of the studies) were endocrine effects (18 studies, 42%), such as virilization, gynecomastia, hormonal disorders, dyslipidemia, genital alterations, and infertility; cardiovascular dysfunctions (six studies, 14%) such as vascular damage, coagulation disorders, and arteriosus hypertension; skin disorders (five studies, 12%) such as pricking, acne, and skin spots; psychiatric and mood disorders (four studies, 9%) such as aggressiveness, sleep disorders and anxiety; musculoskeletal disorders (two studies, 5%), excretory disorders (two studies, 5%), and gastrointestinal disorders (two studies, 5%). Conclusions: Based on the result of our study, the most common adverse effects secondary to the abuse of nandrolone decanoate (ND) involve the endocrine, cardiovascular, skin, and psychiatric systems. These data could prove useful to healthcare professionals in both sports and clinical settings

Accuracy of ChatGPT-Generated Information on Head and Neck and Oromaxillofacial Surgery: A Multicenter Collaborative Analysis

Objective: To investigate the accuracy of Chat-Based Generative Pre-trained Transformer (ChatGPT) in answering questions and solving clinical scenarios of head and neck surgery. Study Design: Observational and valuative study. Setting: Eighteen surgeons from 14 Italian head and neck surgery units. Methods: A total of 144 clinical questions encompassing different subspecialities of head and neck surgery and 15 comprehensive clinical scenarios were developed. Questions and scenarios were inputted into ChatGPT4, and the resulting answers were evaluated by the researchers using accuracy (range 1-6), completeness (range 1-3), and references' quality Likert scales. Results: The overall median score of open-ended questions was 6 (interquartile range[IQR]: 5-6) for accuracy and 3 (IQR: 2-3) for completeness. Overall, the reviewers rated the answer as entirely or nearly entirely correct in 87.2% of cases and as comprehensive and covering all aspects of the question in 73% of cases. The artificial intelligence (AI) model achieved a correct response in 84.7% of the closed-ended questions (11 wrong answers). As for the clinical scenarios, ChatGPT provided a fully or nearly fully correct diagnosis in 81.7% of cases. The proposed diagnostic or therapeutic procedure was judged to be complete in 56.7% of cases. The overall quality of the bibliographic references was poor, and sources were nonexistent in 46.4% of the cases. Conclusion: The results generally demonstrate a good level of accuracy in the AI's answers. The AI's ability to resolve complex clinical scenarios is promising, but it still falls short of being considered a reliable support for the decision-making process of specialists in head-neck surgery

Postoperative Management of Zygomatic Arch Fractures: In-House Rapid Prototyping System for the Manufacture of Protective Facial Shields

Zygomatic fractures account for 10% to 15% of all facial fractures. The surgical management of isolated zygomatic arch fractures usually requires open reduction treatment without fixation through an intraoral access. Therefore, the main problem in the non-fixed treatment of zygomatic arch fractures is related to the difficulty in obtaining a stable reduction for a period long enough to guarantee the physiological bone healing process. We propose an innovative "in-house" rapid prototyping (RP) protocol for the 3D-zygoma mask manufacture of a patient-specific protective device to apply after zygomatic arch fracture reduction. Our study includes 16 consecutive patients who underwent surgical open reduction for an isolated zygoma fracture without fixation between January 2017 and February 2018. The patients received regular postoperative checks at weeks 1 and 2. Before the device was removed, a multiple choice questionnaire was administered to measure the degree of wearability of the mask. The estimated cost of the production is around &OV0556;5 per case and the construction time is around 90 minutes. Based on the encouraging results, obtained in our experience, we hope that other studies can be conducted to confirm our procedure and improve its functionality in the field of facial trauma

Validation of the Quality Analysis of Medical Artificial Intelligence (QAMAI) tool: a new tool to assess the quality of health information provided by AI platforms

Background: The widespread diffusion of Artificial Intelligence (AI) platforms is revolutionizing how health-related information is disseminated, thereby highlighting the need for tools to evaluate the quality of such information. This study aimed to propose and validate the Quality Assessment of Medical Artificial Intelligence (QAMAI), a tool specifically designed to assess the quality of health information provided by AI platforms. Methods: The QAMAI tool has been developed by a panel of experts following guidelines for the development of new questionnaires. A total of 30 responses from ChatGPT4, addressing patient queries, theoretical questions, and clinical head and neck surgery scenarios were assessed by 27 reviewers from 25 academic centers worldwide. Construct validity, internal consistency, inter-rater and test–retest reliability were assessed to validate the tool. Results: The validation was conducted on the basis of 792 assessments for the 30 responses given by ChatGPT4. The results of the exploratory factor analysis revealed a unidimensional structure of the QAMAI with a single factor comprising all the items that explained 51.1% of the variance with factor loadings ranging from 0.449 to 0.856. Overall internal consistency was high (Cronbach's alpha = 0.837). The Interclass Correlation Coefficient was 0.983 (95% CI 0.973–0.991; F (29,542) = 68.3; p < 0.001), indicating excellent reliability. Test–retest reliability analysis revealed a moderate-to-strong correlation with a Pearson’s coefficient of 0.876 (95% CI 0.859–0.891; p < 0.001). Conclusions: The QAMAI tool demonstrated significant reliability and validity in assessing the quality of health information provided by AI platforms. Such a tool might become particularly important/useful for physicians as patients increasingly seek medical information on AI platforms