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Record W2050023941 · doi:10.1155/2012/204623

Chinese-Language Montreal Cognitive Assessment for Cantonese or Mandarin Speakers: Age, Education, and Gender Effects

2012· article· en· W2050023941 on OpenAlexaboutno aff
Ling Zheng, Evelyn L. Teng, Rohit Varma, Wendy J. Mack, Dan Mungas, Po H. Lu, Helena C. Chui

Bibliographic record

VenueInternational Journal of Alzheimer s Disease · 2012
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingNational Eye InstituteUniversity of Southern CaliforniaBattelle
KeywordsMandarin ChineseMontreal Cognitive AssessmentAlgorithmArtificial intelligenceMedicineMachine learningMathematicsComputer scienceCognitionCognitive impairmentPhilosophy

Abstract

fetched live from OpenAlex

The Montreal Cognitive Assessment Chinese-Language Los Angeles version (MoCA-ChLA) was developed and administered during an in-home interview to 1,192 participants (mean age 62.5 years, mean education 11.6 years) in a population-based Chinese American Eye Study (CHES) in Los Angeles. The MoCA-ChLA score (mean ± SD) was 23.8 ± 4.2 with little ceiling and no floor effects. The score increased with higher education, decreased with advancing age, and was not related to gender. Compared to the education 1-6 years group, the mean MoCA-ChLA score was 2.6 and 4.6 higher in the education 7-11 and 12-20 years groups, respectively. The Mandarin- (n = 612) and Cantonese- (n = 612) speaking subgroups performed comparably; Cronbach's alpha of the MoCA-ChLA score was 0.78 and 0.79 for these two groups, respectively. Item response theory analysis showed good discriminating power for executive function and memory. These properties support the MoCA-ChLA as a useful screening tool for aging and dementia studies for Mandarin or Cantonese speakers.

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 score0.112
Threshold uncertainty score0.402

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 head0.024
GPT teacher head0.423
Teacher spread0.399 · 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

Citations55
Published2012
Admission routes1
Has abstractyes

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