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Record W4205640434 · doi:10.1002/alz.055534

The relationship between subjective hearing impairment and mild behavioral impairment – A study of older adults without dementia in the COMPASS‐ND Study

2021· article· en· W4205640434 on OpenAlexaffabout
Dylan X. Guan, Penny Gosselin, Hung‐Yu Chen, M. Kathleen Pichora‐Fuller, Natalie Phillips, Eric E. Smith, Zahinoor Ismail

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsHotchkiss Brain InstituteConcordia UniversityAlberta Health ServicesUniversity of CalgarySimon Fraser UniversityAlberta HealthUniversity of LethbridgeUniversity of Toronto
Fundersnot available
KeywordsDementiaAudiologyCognitionHearing lossCognitive impairmentLogistic regressionCognitive declineApathyPsychologyMedicineClinical psychologyPsychiatryDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Hearing loss and mild behavioral impairment (MBI) are both early warning signs of cognitive decline and dementia in older adults and have been recommended for use as non‐cognitive markers of dementia. To date, few studies have directly investigated the relationship between these two markers. Method Baseline data from 219 non‐demented participants (10 cognitively normal; CN, 48 subjective cognitive decline; SCD, 161 mild cognitive impairment; MCI) in the COMPASS‐ND study (February 2020 release) were analyzed. Hearing impairment was measured in three ways: with a 10‐item self‐report measure using the Hearing Handicap Inventory for the Elderly – Screening Version (HHIE‐S), with a speech and noise test using the Canadian Digit Triplet Test (CDTT), and with screening audiometry using 2 discrete input levels at 2000 Hz to generate 6 hearing loss categories. Global and domain‐specific MBI burden was approximated using the Neuropsychiatric Inventory Questionnaire (NPI‐Q) with a published algorithm. Multivariable linear regressions were conducted to examine the association between the three hearing impairment measures and global MBI burden, adjusting for sex, age, education, hearing aid use, and Montreal Cognitive Assessment (MoCA) score or diagnosis. Multivariable logistic regressions were used to investigate whether the hearing variables could predict MBI domains. Result Half of all participants showed MBI symptoms (Figure 1). Greater self‐reported hearing impairment measured by the HHIE‐S was significantly associated with greater global MBI burden and the presence of apathy and affective dysregulation when controlling for global cognition or diagnosis (Table 1). These associations remained significant in analyses restricted to MCI alone. Performance on CDTT and screening audiometry, were not associated with global or domain‐specific MBI burden. Conclusion The HHIE‐S, which was designed to capture the emotional and social aspects of hearing loss, was positively related to global MBI burden and more specifically to apathy and affect. Unlike audiometry and speech and noise measures, self‐reported measures of hearing impairment can be influenced by age, sex, other comorbidities, and social factors. Our findings underscore that value of self‐report measures of hearing impairment as distinct from audiometry and speech and noise measures in their association with behavioral impairment and as non‐cognitive markers of dementia.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.346
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2021
Admission routes2
Has abstractyes

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