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Record W2139320122 · doi:10.1093/geronb/gbs038

The Effect of Bilingualism on Amnestic Mild Cognitive Impairment

2012· article· en· W2139320122 on OpenAlexafffund
Lynn Ossher, Ellen Bialystok, Fergus I. M. Craik, Kelly J. Murphy, Angela K. Troyer

Bibliographic record

VenueThe Journals of Gerontology Series B · 2012
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest HospitalUniversity of TorontoYork University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentCanadian Institutes of Health Research
KeywordsNeuroscience of multilingualismDementiaNeuropsychologyPsychologyAudiologyCognitionCognitive impairmentDevelopmental psychologyMedicinePsychiatryNeuroscienceDiseasePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: Previous reports have found that lifelong bilingualism is associated with a delay in the onset of dementia, including Dementia of the Alzheimer's Type (DAT). Because amnestic mild cognitive impairment (aMCI) is often a transition stage between normal aging and DAT, our aim in this paper was to establish whether this delay in symptom onset for bilinguals would also be seen in the onset of symptoms of aMCI and whether this delay would be consistent in different subtypes of aMCI. METHOD: We examined the effect of bilingualism on the age of diagnosis in individuals with single- or multiple-domain aMCI who were administered a battery of neuropsychological tests and questionnaires about their language and social background. RESULTS: Our results showed an interaction between aMCI type and language history. Only individuals diagnosed with single-domain aMCI demonstrated a later age of diagnosis for bilinguals (M = 79.4 years) than monolinguals (M = 74.9 years). DISCUSSION: This preliminary evidence suggests that the early protective advantage of bilingualism may be specific to single-domain aMCI, which is the type of aMCI most specifically associated with progression to DAT.

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)0.003
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 score0.147
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.388
Teacher spread0.348 · 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

Citations74
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series BSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207