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Record W4415223217 · doi:10.1080/14992027.2025.2573033

The impact of cognition on hearTest administration in older adults receiving home care

2025· article· en· W4415223217 on OpenAlexaboutno aff
Helen Gurteen, Melinda Toomey, L. Wilson, Bruno Chaves Franco, Yuanyuan Gu, Chyrisse Heine, Sabrina Lenzen, Piers Dawes

Bibliographic record

VenueInternational Journal of Audiology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsnot available
FundersNational Health and Medical Research Council
KeywordsCognitionAdministration (probate law)PopulationTest (biology)Cognitive impairmentCognitive testActivities of daily livingCognitive disabilities

Abstract

fetched live from OpenAlex

Objective This study investigated the impact of cognitive function on the administration of the hearTest automated hearing test in older adults. The relationship between Montreal Cognitive Assessment (MoCA) score and three hearTest metrics (false response rate, standard deviation of response time, and test duration) was analysed.Design A cross-sectional correlational design was used. Testing was conducted in participants’ homes or retirement villages.Study Sample One hundred and five older adults (aged 67–97 years) receiving home-based aged care with MoCA scores ranging between 5 (possible dementia) and 30 (healthy cognition).Results There was no relationship between MoCA score and false response rate (r = −0.12, CI = −0.42 to 0.06, p = 0.22) or standard deviation of response time (r = −0.11, CI = −0.33 to 0.04, p = 0.27). There was a moderate sized correlation between MoCA score and test duration (r = −0.31, CI = − 0.49 to − 0.15, p = 0.001), indicating longer test duration with lower MoCA scores.Conclusions hearTest performance is not impacted by cognitive ability in a population of older adults that included people living with dementia. However, additional test time may be needed for hearTest administration for individuals with cognitive impairment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.350
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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