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Record W2335138188 · doi:10.1097/wad.0000000000000019

Is Bilingualism Associated With a Lower Risk of Dementia in Community-living Older Adults? Cross-sectional and Prospective Analyses

2014· article· en· W2335138188 on OpenAlexaff
Caleb M. Yeung, Philip D. St. John, Verena Menec, Suzanne L. Tyas

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of WaterlooUniversity of Manitoba
Fundersnot available
KeywordsDementiaNeuroscience of multilingualismCross-sectional studyGerontologyPsychologyMedicineEnvironmental healthNeuroscience

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to determine whether bilingualism is associated with dementia in cross-sectional or prospective analyses of older adults. METHODS: In 1991, 1616 community-living older adults were assessed and were followed 5 years later. Measures included age, sex, education, subjective memory loss (SML), and the modified Mini-mental State Examination (3MS). Dementia was determined by clinical examination in those who scored below the cut point on the 3MS. Language status was categorized based upon self-report into 3 groups: English as a first language (monolingual English, bilingual English) and English as a Second Language (ESL). RESULTS: The ESL category had lower education, lower 3MS scores, more SML, and were more likely to be diagnosed with cognitive impairment, no dementia at both time 1 and time 2 compared with those speaking English as a first language. There was no association between being bilingual (ESL and bilingual English vs. monolingual) and having dementia at time 1 in bivariate or multivariate analyses. In those who were cognitively intact at time 1, there was no association between being bilingual and having dementia at time 2 in bivariate or multivariate analyses. CONCLUSIONS: We did not find any association between speaking >1 language and 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 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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.315
Teacher spread0.290 · 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.

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

Citations75
Published2014
Admission routes1
Has abstractyes

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