Impact of Hearing Loss on Geriatric Assessment
Bibliographic record
Abstract
BACKGROUND: Due to the aging society, the incidence of age-related hearing loss (ARHL) is strongly increasing. Hearing loss has a high impact on various aspects of life and may lead to social isolation, depression, loss of gain control, frailty and even mental decline. Comorbidity of cognitive and sensory impairment is not rare. This might have an impact on diagnostics and treatment in the geriatric setting. OBJECTIVE: The aim of the study was to evaluate the impact of hearing impairment on geriatric assessment and cognitive testing routinely done in geriatrics. MATERIAL AND METHODS: This review is based on publications retrieved by a selective search in Medline, including individual studies, meta-analyses, guidelines, Cochrane reviews, and other reviews from 1960 until August 2020. RESULTS: Awareness of sensory impairment is low among patients and health professionals working with elderly people. The evaluation of the hearing status is not always part of the geriatric assessment and not yet routinely done in psychiatric settings. However, neurocognitive testing as an important part can be strongly influenced by auditory deprivation. Misunderstanding of verbal instructions, cognitive changes, and delayed central processes may lead to a false diagnosis in up to 16% of subjects with hearing loss. To minimize this bias, several neurocognitive assessments were transformed into non-auditory versions recently, eg the most commonly used Hearing-Impaired Montreal Cognitive Assessment (HI-MoCA). However, most of them still lack normative data for elderly people with hearing loss. CONCLUSION: Hearing loss should be taken into consideration when performing geriatric assessment and cognitive testing in elderly subjects. Test batteries suitable for ARLH should be applied.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".