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Record W4414152558 · doi:10.1080/13607863.2025.2556751

Sensory impairments and cognitive impairment among Mexican American older adults: nativity differences

2025· article· en· W4414152558 on OpenAlexaff
Bibiana Toro Figueira, Soham Al Snih

Bibliographic record

VenueAging & Mental Health · 2025
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsThe Quebec Population Health Research Network
FundersNational Institute on Minority Health and Health DisparitiesNational Institute on Aging
KeywordsMexican americansCognitive impairmentCognitionVisual impairmentImmigrationActivities of daily livingEthnic groupHearing lossDementia

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the associations between sensory impairments and cognitive impairment (CI), and how these associations differ by nativity over 12 years of follow-up among Mexican American 75 years and older with moderate to high cognitive function at baseline. METHOD: Dual sensory impairment (DSI) included vision impairment (VI), difficulty in recognizing a friend at arm's length, across the room, or across the street); and hearing impairment (HI), inability to hear and understand a speech without seeing a person talk, in a quiet room. Participants were grouped into No VI-No HI, HI only, VI only, and Yes VI-Yes HI by nativity. CI was defined as scoring <21 on the Mini-Mental State Examination. RESULTS: US-born and foreign-born participantsin the Yes VI-Yes HI group and US-born in the VI only group had greater odds of CI over time than those without VI and without HI (OR = 2.64, 95%CL = 1.23-5.68, OR = 5.71, 95%CL = 2.78-11.73; and OR = 2.09, 95%CL = 1.28-3.43, respectively), after controlling for covariates. CONCLUSION: US-born and foreign-born Mexican American older adults with DSI were at high risk of developing CI over time. Addressing hearing and vision impairments may counteract CI over time.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.255
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.321
Teacher spread0.308 · 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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