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Record W4386393034 · doi:10.5770/cgj.26.669

Dual Sensory Impairment and Functional Status in a Prospective Cohort Study*

2023· article· en· W4386393034 on OpenAlexafffundvenueabout
Amber Janower, Phil St. John

Bibliographic record

VenueCanadian Geriatrics Journal · 2023
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Manitoba
FundersHealth CanadaCanadian Geriatrics Society
KeywordsMedicineVisual impairmentOdds ratioConfidence intervalFunctional impairmentAudiologyCohort studyCohortProspective cohort studyGerontologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Objective: To examine the impact of visual impairment, hearing impairment, and dual sensory impairment (DSI) on functional status in older adults. Methods: Secondary analysis of the Manitoba Health and Aging Study, a population-based cohort study of 1751 adults age 65+. Data were collected from 1991 to 1992 (Time 1), with follow-up five years later (Time 2). Vision and hearing were self-reported. Functional status was measured using the Older Americans Resource and Services (OARS). Logistic regression models were constructed to assess functional status at both Time 1 and Time 2. Results: Dual sensory impairment (DSI) at Time 1 predicted poor functional status at both Time 1 and Time 2. The adjusted odds ratios (OR; 95% confidence interval [CI]) for poor functional status at Time 1 for those with only hearing impairment was 1.74 (1.25, 2.44) for visual impairment was 2.95 (2.19, 3.98), and for DSI was 3.58 (2.58, 4.95). At Time 2, the adjusted ORs for poor functional status for those with only hearing impairment was 1.32 (0.86, 2.03), for visual impairment was 1.63 (1.05, 2.52), and for DSI was 2.61 (1.54, 4.40). Conclusions: DSI is associated with lower functional status, but the effect of visual impairment is more pronounced than hearing 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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.024
GPT teacher head0.260
Teacher spread0.236 · 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

Citations4
Published2023
Admission routes4
Has abstractyes

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