Background/Objectives: A sizeable population of patients with normal pure-tone audiograms endorse a consistent difficulty of following conversations in noisy environments. Termed hidden hearing loss (HHL), this condition evades traditional diagnostic methods for hearing loss and thus is significantly under-diagnosed and untreated. This review sought to identify emerging methods of diagnosing HHL via measurement of its histopathologic correlate: cochlear synaptopathy, the loss of synapses in the auditory nerve pathway. Methods: A thorough literature search of multiple databases was conducted to identify studies with objective, electrophysiological measures of synaptopathy. The PRISMA protocol was employed to establish criteria for the selection of relevant literature. Results: A total of 21 studies were selected with diagnostic methods, including the auditory brainstem response (ABR), electrocochleography (EcochG), middle ear muscle reflex (MEMR), and frequency-following response (FFR). Measures that may indicate the presence of synaptopathy include a reduced wave I amplitude of ABR, reduced SP amplitude of EcochG, and abnormal MEMR, among other measurements. Behavioral measures were often performed alongside electrophysiological measures, the most common of which was the speech-in-noise assessment. Conclusions: ABR was the most common diagnostic method for assessing HHL. Though ABR, EcochG, and MEMR may be sensitive to measuring synaptopathy, more literature comparing these methods is necessary. A two-pronged approach combining behavioral and electrophysiological measures may prove useful as a criterion for diagnosing and estimating the extent of pathology in affected patients.

Background/Objectives: This study aimed to develop a machine learning (ML) algorithm that can predict unplanned reoperations and surgical/medical complications after vestibular schwannoma (VS) surgery. Methods: All pre- and peri-operative variables available in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (n = 110), except those directly related to our outcome variables, were used as input variables. A deep neural network model consisting of seven layers was developed using the Keras open-source library, with a 70:30 breakdown for training and testing. The feature importance of input variables was measured to elucidate their relative permutation effect in the ML model. Results: Of the 1783 patients with VS undergoing surgery, unplanned reoperation, surgical complications, and medical complications were seen in 8.5%, 5.2%, and 6.2% of patients, respectively. The deep neural network model had area under the curve of receiver operating characteristics (ROC-AUC) of 0.6315 (reoperation), 0.7939 (medical complications), and 0.719 (surgical complications). Accuracy, specificity, and negative predictive values of the model for all outcome variables ranged from 82.1 to 96.6%, while positive predictive values and sensitivity ranged from 16.7 to 51.5%. Variables such as the length of stay post-operation until discharge, days from operation to discharge, and the total hospital length of stay had the highest permutation importance. Conclusions: We developed an effective ML algorithm predicting unplanned reoperation and surgical/medical complications post-VS surgery. This may offer physicians guidance into potential post-surgical outcomes to allow for personalized medical care plans for VS patients.

Background: Sex differences in Alzheimer's disease (AD) are well recognized, yet their implications for cognitive assessment remain unclear. Females often demonstrate better cognitive performance than males despite comparable levels of neurodegeneration, which may delay diagnosis. Objective: To evaluate sex differences in cognitive performance across AD continuum. Methods: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we examined sex differences in total and item-level performance on the 13-item Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS-Cog-13) among cognitively normal (CN) individuals and patients with AD. Regression models were performed, adjusting for hippocampal parenchymal fraction, age, and education. Results: The study included 656 CN participants (59% female) and 193 AD patients (45% female). Among CN individuals, female sex was associated with lower total ADAS-Cog-13 scores (β = −2.09, p < 0.0001). CN females demonstrated superior performance on multiple verbal subdomains, including word recall, delayed word recall, naming, and word recognition. In contrast, no significant sex difference was observed in total ADAS-Cog-13 scores among AD patients, although males performed better than females on delayed word recall. Across both groups, greater hippocampal integrity was strongly associated with better global and domain-specific cognitive performance. Conclusions: Sex differences in ADAS-Cog-13 performance are evident in cognitively normal individuals but largely attenuated in AD patients. Superior verbal performance in CN females, independent of hippocampal integrity, may mask early cognitive decline and contribute to delayed diagnosis. These findings highlight the importance of accounting for sex when interpreting cognitive test results, particularly in preclinical stages of AD.

Objective: To propose that migraine-related symptoms such as dizziness, sudden hearing loss, tinnitus, and vertigo—when occurring without headache—should be recognized as manifestations of central sensitivity syndrome (CSS), and to explore the implications of this reclassification for clinical practice and patient care. Data sources: PubMed Central and Google Scholar. Review methods: A search of the literature was performed using PubMed and Google Scholar. Search terms included combinations of keywords such as “migraine”, “vertigo”, “tinnitus”, “dizziness”, “sudden hearing loss”, “central sensitivity syndrome”, and “central sensitization”. Conclusions: Non-headache migraine symptoms show significant overlap with characteristics of CSS, including central nervous system hyperexcitability and dysregulation. Neuroimaging and clinical data support this connection, suggesting these symptoms may be better understood within the CSS framework. Recognizing this association could represent a conceptual shift in how such symptoms are classified and managed. Implications for practice: Incorporating non-headache migraine symptoms into the CSS paradigm may lead to earlier recognition, reduce misdiagnosis and stigma, and support the development of more effective, targeted therapeutic strategies for affected patients.