Journal of Otorhinolaryngology, Hearing and Balance Medicine

Introduction: Acute vertigo presents a diagnostic challenge in the acute stroke setting. Accurate differentiation between a posterior circulation stroke and benign peripheral vestibular disorders is critical for appropriate, timely management. This case highlights the value of a multidisciplinary team approach, including a vestibular physiotherapy assessment and stroke physicians, in a complex acute vertigo presentation. Case Presentation: Mr. G, a 68-year-old male with a past medical history of stroke and hypertension, presented to the Emergency Department with acute-onset vertigo, severe vomiting, and imbalance. He described his symptoms as similar to his previous stroke, raising immediate concern for recurrent cerebrovascular events. Initial CT brain imaging showed no acute pathology. Admission to the hyper-acute stroke unit followed, where standard neurological examination was unremarkable. A HINTS examination and Dix–Hallpike testing performed by the admitting stroke consultant did not yield a definitive diagnosis. Specialist vestibular physiotherapy assessment identified subjective clues and, through systematic positional testing, suggested left apogeotropic horizontal-canal BPPV. The patient was treated successfully with three Gufoni manoeuvres over 24 h. Mr. G was discharged home on a virtual ward, with a subsequent MRI confirming no acute stroke, and telephone follow-up confirming full resolution of symptoms. Conclusions: This case demonstrates the importance of systematic progression through vestibular diagnostic testing and the value of specialist physiotherapy in acute hospital settings. Subjective clues, including positional triggers and symptom exacerbation during CT scanning, contributed decisively to a more likely accurate diagnosis, enabling timely evidence-based treatment and safe, early discharge.

Background/Objectives: To characterize the demographics, inpatient resource utilization, and coded outcomes of pediatric vestibular schwannoma hospitalizations using a national database. Because the Kids’ Inpatient Database records discharges rather than individuals, all measures are reported at the level of the hospital encounter rather than the unique patient. Methods: This was a retrospective cross-sectional study. We analyzed data from the Kids’ Inpatient Database (2003–2019) for patients aged 0–20 years hospitalized with a diagnosis of vestibular schwannoma. Demographics, hospital characteristics, coded adverse diagnoses, surgical intervention, length of stay (LOS), and total charges were assessed. Bivariate and multivariable analyses examined factors associated with surgical resection of vestibular schwannoma. Results: We identified 347 hospitalizations; median age was 16 years and 54.5% were female. Most admissions were for patients who were White (58.5%) and privately insured (68.6%). Surgery occurred in 12.4% of admissions. Surgical stays had longer median LOS (5 vs. 4 days, p = 0.0035) and higher median charges ($82,208.50 vs. $50,723, p = 0.0002). Rates of coded adverse diagnoses during hospitalization were low: facial nerve injury, 12.7%; sensorineural hearing loss, 3.5%; cerebrospinal fluid leak, 0.6%; postoperative infection, 0.9%; and in-hospital mortality, 0.29%. An NF2 diagnosis code was present in 21.9% of hospitalizations, reflecting coded diagnoses rather than true NF2 prevalence, and was more frequent among patients younger than 18 years than among those aged 18–20 years (27.1% vs. 11.9%, p < 0.001). In bivariate comparisons, hydrocephalus (18.6% vs. 8.2%, p = 0.0466) and vagus nerve injury or vocal fold paralysis (9.3% vs. 2.6%, p = 0.0483) were more common among surgical patients. In Firth’s penalized logistic regression, no clinical, demographic, or hospital factor reached significance as an independent predictor of surgery, although the analysis was likely underpowered to detect such associations; trends were observed for vagus nerve injury (OR 4.39, p = 0.065), seizures (OR 2.75, p = 0.077), and vertigo (OR 0.15, p = 0.075). Conclusions: Pediatric vestibular schwannoma hospitalizations are rare, and most admissions did not involve surgical resection during the index hospitalization. When performed, surgery was associated with greater resource use but low rates of coded adverse diagnoses and in-hospital mortality. No factor reached significance as an independent predictor of surgery in the adjusted models, which were likely underpowered given the small number of surgical encounters. Future work linking clinical detail to longitudinal outcomes is needed to refine surgical selection and evaluate potential disparities in care.

Background/Objectives: Artificial intelligence (AI) tools are used in clinical education and patient communication. Concordance with American Academy of Otolaryngology–Head and Neck Surgery Foundation clinical practice guidelines (CPG) and overall readability remain poorly defined. Methods: Over 2 months, ChatGPT, Google Gemini, and Google Search AI were queried using a single LF prompt and a layered set of sub-questions. Responses were scored with a 12-point rubric from the CPG plain-language tonsillectomy guideline. Readability was assessed using Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL). Temporal trends and between-group differences were analyzed using regression and repeated-measures methods. Results: Layered prompts demonstrated greater alignment with guideline subtopics than LF (89.5% vs. 71.4%, p < 0.001). For LF queries, mean concordance was 74.5% (95% CI 72.0–77.0) for Google AI, 71.3% (68.9–73.7) for Gemini, and 68.4% (66.0–70.9) for ChatGPT. Overall, Google AI outperformed Gemini and ChatGPT on pairwise comparisons (p = 0.001). Google AI demonstrated improvement in concordance (slope = 0.03, p = 0.003), while Gemini showed no change (p > 0.05) and ChatGPT worsened over time (slope = −0.03, p < 0.001). Readability differed by strategy and model. Layered prompts produced more accessible text (FKGL 44.6, FRE 10.3) compared with LF outputs (FKGL 35.2, FRE 11.3) and the CPG (FKGL 10.6, FRE 41.7). IRR was high (ICC = 0.96). Conclusions: Model type and prompt structure shaped the quality of AI-generated tonsillectomy guidance. Google AI consistently outperformed ChatGPT, with Gemini intermediate, and uniquely showed temporal improvement. Layered prompting produced more CPG-aligned and readable responses across all models. Readability was consistently above a 7th grade level.