Cognitive assessment in hearing aid clinics: Is it feasible to implement in a National Health Service (NHS) setting?
Bibliographic record
Abstract
BackgroundCognitive impairment is common in older adults and negatively affects hearing aid use. Audiologists hold the opportunity to identify signs of undiagnosed cognitive impairment and tailor care to optimise hearing aid use.ObjectiveTo assess the feasibility of introducing a brief cognitive assessment in hearing aid appointments for older adults.MethodsProspective feasibility cohort study incorporating quantitative and observational data. Participants were patients aged ≥65 years, new or existing hearing aid users, attending an NHS community hospital hearing aid clinic. Clinical audiologists were trained to conduct the Ascertain Dementia 8 (AD8) and visually-adapted shortened version of the Montreal Cognitive Assessment (mini-MoCA). A research audiologist took informed consent, observed appointments recording outcomes and followed up participants at 3 months. Feasibility was assessed using the following outcome measures: practicality of implementation in a clinical setting and resource requirements; acceptability in terms of recruitment/completion rates; onward care; experiences through standardised intensity scoring of observed emotions and analysis of free-text observations of participant reactions, participants' comments and informal conversations with clinical audiologists.ResultsTwenty patients were recruited, average age 78.6 years, 14 (70%) attended alone. All completed cognitive assessment, average duration was 14 minutes. AD8 and mini-MoCA average scores were 2.4 (range: 0-7) and 12.8 (range: 8-15), respectively. Ten (50%) participants had AD8 scores and one (5%) a Mini-MoCA score indicating potential cognitive impairment. Four of those (40%) contacted their GP, three were referred for further cognitive evaluation, one was diagnosed with dementia, two were awaiting appointments.ConclusionsIntroducing cognitive assessment in hearing aid clinics seems feasible and may provide an opportunity for identifying cognitive impairment in older adults, though further research is needed to establish its clinical utility and impact on care pathways. There are considerable resource implications, highlighting the importance of involving professional organisations, healthcare funders and policy makers early in this discussion.
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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 | 0.058 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".