During the COVID-19 pandemic, the utility of portable audiometry became more apparent as elective procedures were deferred in an effort to limit exposure to health care providers. Herein, we retrospectively evaluated mobile-based audiometry in the emergency department and outpatient otology and audiology clinics. Air conduction thresholds with mobile audiometry were within 5 dB in 66% of tests (95% CI, 62.8%-69.09%) and within 10 dB in 84% of tests (95% CI, 81.4%-86.2%) as compared with conventional audiometry. No significant differences were noted between mobile-based and conventional audiometry at any frequencies, except 8 kHz ( P < .05). The sensitivity and specificity for screening for hearing loss were 94.3% (95% CI, 91.9%-96.83%) and 92.3% (95% CI, 90.1%-94.4%), respectively. While automated threshold audiometry does not replace conventional audiometry, mobile audiometry is a promising screening tool when conventional audiometry is not available.

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HINTS (head impulse, nystagmus, and test of skew) protocol is widely used at emergency rooms and outpatients’ settings to differentiate central from peripheral vertigo. Doctors usually get experience during their training on the basic concepts of the test. However, there is a lack of information about the current understanding of the test and its application by future practitioners, who will be the first responders to vertigo patients. We conducted a quasi-experimental study to assess the understanding, applicability, and comprehension of HINTS in medical students after a theory and practice session and follow-up 1-month after. Twenty-one students were evaluated with pre-test and post-tests. Comprehension (Δ40%), understanding (Δ60%) and applicability (Δ48%) were increased after the session. Head impulse (Δ 39%), nystagmus evaluation (Δ 10%) and test of skew (Δ 39%) showed a better understanding and comprehension even 1-month after. Findings have educational implications of this protocol in future healthcare professionals.

ObjectiveIn an era of vestibular schwannoma (VS) surgery where functional preservation is increasingly emphasized, persistent postoperative dizziness is a relatively understudied functional outcome. The primary objective was to develop a predictive model to identify patients at risk for developing persistent postoperative dizziness after VS resection.MethodsRetrospective review of patients who underwent VS surgery at our institution with a minimum of 12 months of postoperative follow‐up. Demographic, tumor‐specific, preoperative, and immediate postoperative features were collected as predictors. The primary outcome was self‐reported dizziness at 3‐, 6‐, and 12‐month follow‐up. Binary and multiclass machine learning classification models were developed using these features.ResultsA total of 1,137 cases were used for modeling. The median age was 67 years, and 54% were female. Median tumor size was 2 cm, and the most common approach was suboccipital (85%). Overall, 63% of patients did not report postoperative dizziness at any timepoint; 11% at 3‐month follow‐up; 9% at 6‐months; and 17% at 12‐months. Both binary and multiclass models achieved high performance with AUCs of 0.89 and 0.86 respectively. Features important to model predictions were preoperative headache, need for physical therapy on discharge, vitamin D deficiency, and systemic comorbidities.ConclusionWe demonstrate the feasibility of a machine learning approach to predict persistent dizziness following vestibular schwannoma surgery with high accuracy. These models could be used to provide quantitative estimates of risk, helping counsel patients on what to expect after surgery and manage patients proactively in the postoperative setting.Level of evidenceLevel IV Laryngoscope, 2023