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The application of the Montreal cognitive assessment for elderly in China

2008· article· en· W3029009750 on OpenAlexaboutno aff
Xueqin Liu, Lixiu Zhang

Bibliographic record

VenueZhonghua xingwei yixue yu naokexue zazhi · 2008
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitive impairmentCognitionInternal medicineMedicineRecallOrientation (vector space)KappaPsychologyPsychiatryMathematicsCognitive psychology

Abstract

fetched live from OpenAlex

Objective To explore the cognitive changes of patients with MCI using the Montreal Cognitive Assessment(MOCA) for elderly preliminarily.Methods 85 normal controls (NC),117 subjects with mild cognitive impairment (MCI) and 73 patients with Alzheimer's disease (AD) were assessed with the MOCA.Results There were significant differences among the three groups in all items of MOCA(F=258.66,P<0.01).Significant differences were observed in almost all the sub-tests between MCI group and NC group or MCI group and AD group(visuopatial F=54.86,P<0.01;naming F=17.30,P<0.01;attention F=82.50,P<0.01;language F=25,88,P<0.01;abstraction F=15.00 ,P<0.01;delayed recall F=130.49,P<0.01;orientation F=176.09,P<0.01.).The most significant differences were found in delayed recall and orientation among three groups(F=176.09,P<0.01;F=130.49,P<0.01.).At 26 cut-point,The results of screening of the MOCA agree with the gold standard of clinical diagnose for the patient with MCI.The agreement rate for observation was 0.93.The agreement rate by chance was 0.61.The Kappa was 0.85.Conclusions The MOCA appropriately define MCI,NC and AD in their cognitive function,it has good discriminant validity.The test of delayed recall and orientation may be more sensitive in the detection of the older people.The MOCA is a useful screening instrument for the patient with MCI. Key words: Montreal Cognitive Assessment(MOCA); Mild cognitive 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)0.000
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 score0.246
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.327
Teacher spread0.313 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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