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Record W2063431068 · doi:10.1097/wad.0b013e3181631517

Use of Clinical Dementia Rating in Detecting Early Cognitive Deficits in a Community-based Sample of Chinese Older Persons in Hong Kong

2008· article· en· W2063431068 on OpenAlexaboutno aff
Linda Lam, Cindy W. C. Tam, Victor Lui, Wai Chi Chan, Sandra Sau Man Chan, Kin Sang Ho, Wai Man Chan, Helen Chiu

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2008
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersChinese University of Hong KongUniversity of Hong Kong
KeywordsClinical Dementia RatingDementiaConcordanceCognitionRating scalePsychologyGerontologyCognitive testAnosognosiaRecallMedicineNeuropsychologyMontreal Cognitive AssessmentCognitive impairmentClinical psychologyDiseasePsychiatryDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

With increasing demand for dementia care in the Chinese community, there is a pressing need to identify practical and valid assessment tool for early detection of dementia. In a sample of 473 randomly recruited community-dwelling Chinese older persons aged 60 or above, we evaluated the cognitive characteristics of subjects with Clinical Dementia Rating (CDR) of 0.5. The cognitive profiles of CDR 0.5 subjects were compared with standard clinical criteria for mild cognitive impairment. The Alzheimer's disease assessment scale-cognitive subscale and list learning delay recall test scores were between -1 and -2 SD below the cutoff for clinically not-demented subjects (CDR 0). Concordance between CDR 0.5 and mild cognitive impairment classifications were related to educational level of the subjects. A higher agreement was found in subjects having >6 years of education than subjects having <or=2 years of education (85.2% vs. 53.8%) (chi2=35.41, df=2, P<0.0001). The results suggested that CDR is able to identify mild but significant cognitive impairment in the Chinese community. The use of CDR suggested that attention should be paid to local cultural characteristics. With the use of cognitive evaluation, special adjustments are required to fit the performance of the respondents with different educational background.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.077
GPT teacher head0.369
Teacher spread0.291 · 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

Citations30
Published2008
Admission routes1
Has abstractyes

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