SELF-REPORTED AND OBJECTIVE ASSESSMENT OF HEARING HANDICAP AND COGNITIVE CHALLENGES IN AGE-RELATED HEARING LOSS
Bibliographic record
Abstract
Abstract Age-related hearing loss (ARHL) is a common condition in older adults, with most reporting challenges in recognizing speech-in-noise (SiN). Growing evidence also indicates cognitive alterations in this population. Concomitant examination of hearing and cognitive abilities using a combination of subjective and objective measures would be useful in characterizing the challenges faced by individuals with ARHL. We examined hearing abilities and cognitive functions in 15 individuals with bilateral mild-to-moderate untreated sensorineural ARHL (age: 70.4 ± 7.4 years, pure tone average: 31.0 ± 3.8 dB HL) and 15 age- and education-matched normal hearing controls (age: 65.7 ± 5.8 years, pure tone average: 15.4 ± 5.9 dB HL). We examined subjective ratings of hearing handicap (Hearing Handicap Inventory for Adults) and cognitive ability (Self-rating of Cognition in Everyday Activities), and objective measures of SiN (Quick Speech-In-Noise) and cognition (Montreal Cognitive Assessment [MoCA] and MoCA-memory index score [MoCA-MIS]). Our analysis revealed that individuals with ARHL self-reported a greater number of cognitive issues in everyday activities and had a higher hearing handicap score (p=.044) relative to controls. Additionally, the ARHL group performed significantly worse on SiN (p=.005) and cognitive screening (MoCA, p=.031; MoCA-MIS, p=.039). Taken together, these findings suggest that in addition to experiencing challenges with hearing abilities, individuals with mild-to-moderate ARHL also experience cognitive changes. Furthermore, these challenges are self-perceived as well as noticeable on objective testing. Our work points towards the consideration of self-report and objective assessment of cognition in geriatric hearing care, which can further inform the development of novel interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".