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Record W3006303631 · doi:10.1097/wad.0000000000000373

Conversion of Mild Cognitive Impairment to Alzheimer Disease in Monolingual and Bilingual Patients

2020· article· en· W3006303631 on OpenAlexaff
Matthias Berkes, Ellen Bialystok, Fergus I. M. Craik, Angela K. Troyer, Morris Freedman

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest HospitalUniversity of TorontoYork University
FundersNational Institute on Aging
KeywordsMemory clinicCognitive reserveAlzheimer's diseaseCognitionAudiologyCognitive impairmentNeuroscience of multilingualismPsychologyDiseaseSignificant differenceMedical diagnosisMedicinePediatricsGerontologyPsychiatryClinical psychologyInternal medicineNeurosciencePathology

Abstract

fetched live from OpenAlex

PURPOSE: Conversion rates from mild cognitive impairment (MCI) to Alzheimer disease (AD) were examined considering bilingualism as a measure of cognitive reserve. METHODS: Older adult bilingual (n=75) and monolingual (n=83) patients attending a memory clinic who were diagnosed with MCI were evaluated for conversion to AD. Age of MCI and AD diagnoses and time to convert were recorded and compared across language groups. PATIENTS: Patients were consecutive patients diagnosed with MCI at a hospital memory clinic. RESULTS: Bilingual patients were diagnosed with MCI at a later age than monolingual patients (77.8 and 75.5 y, respectively), a difference that was significant in some analyses. However, bilingual patients converted faster from MCI to AD than monolingual patients (1.8 and 2.8 y, respectively) resulting in no language group difference in age of AD diagnosis. This relationship held after accounting for education, cognitive level, immigration status, and sex. DISCUSSION: The findings suggest that greater cognitive reserve as measured by language status leads to faster conversion between MCI and AD, all else being equal.

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)0.001
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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

Citations35
Published2020
Admission routes1
Has abstractyes

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