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Record W4206696152 · doi:10.1002/alz.053986

The use of cognitive measures in older adults with concurrent hearing and vision impairment: A scoping review

2021· review· en· W4206696152 on OpenAlexaff
Shirley Dumassais, Gabrielle Aubin, Atul Jaiswal, Shikha Gupta, Sangeetha Santhakumaran, Walter Wittich

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typereview
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalMAB-Mackay Rehabilitation CentreCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre intégré de santé et de services sociaux de la Montérégie-CentreMcGill UniversityCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre Intégré de Santé et de Services Sociaux des LaurentidesUniversité de Montréal
Fundersnot available
KeywordsCognitionDementiaPsycINFOCINAHLClinical psychologyCognitive testPopulationMEDLINEPsychologyMini–Mental State ExaminationGerontologyMedicineDiseaseCognitive impairmentPsychiatryPsychological intervention

Abstract

fetched live from OpenAlex

Abstract Background Older adults with dual sensory impairment (DSI/combined hearing and visual impairment), are more likely to obtain poorer scores on cognitive tests. Whether these results are due to poor cognitive function or inappropriate or absent adaptation of test administration for sensory impaired older adults is unclear. Given that this population is at higher risk of developing dementia, e.g., due to Alzheimer’s disease, ensuring optimal test administration is imperative to provide adequate cognitive care. Therefore, we mapped the existing scientific literature on various cognitive measures/tools that have been used to screen or assess cognitive impairment in older adults with DSI. Method A scoping review was conducted using the Arksey and O’Malley framework (2005). Scientific articles were searched across eleven databases (CINAHL, Embase, Global Health, Mednar, OAIster, OpenGrey, PsycEXTRA, PsycINFO, PubMed, Web of Science, and WorldWideScience). The inclusion criteria were: the articles considered and/or measured cognitive function, in individuals with DSI, aged 65 and older. Extracted data included sample cognitive status, cognitive measure type (objective or subjective), reported cognitive domains evaluated by the measure, psychometric properties as well as reported, if any, sensibility measures during administration. Result Of 11,595 articles retrieved, fifty‐six (n = 56) met the inclusion criteria. All but five (n=5) studies employed an objective measure of cognitive function. The most commonly used cognitive test in studies with older adults with DSI is the Mini‐Mental State Examination (1975) with almost 1/3 of the studies reporting its use (n=17). More importantly, none of the studies in this scoping review used cognitive tests that were adapted for vision and hearing impairment, highlighting that optimal cognitive assessment in DSI is neglected. Conclusion Most objective cognitive tests include vision‐ and hearing‐dependent items and require functional vision and/or hearing for both the administration and the execution of these tests. Our study confirmed the use of commonly used standardized tests, initially validated in a healthy population, to measure the cognitive function of older adults with DSI. Identifying these generally utilized tools is the first step to developing optimal adaptations in their administration for this vulnerable population.

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)0.015
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.086
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0230.020
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.123
GPT teacher head0.386
Teacher spread0.264 · 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 designSystematic review
Domainnot available
GenreReview

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 routes1
Has abstractyes

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