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Record W2014210415 · doi:10.1177/0891988714524630

Validity of the Montreal Cognitive Assessment as a Screen for Mild Cognitive Impairment and Dementia in African Americans

2014· article· en· W2014210415 on OpenAlexaboutno aff
Felicia C. Goldstein, Angela V. Ashley, Eric N. Miller, Olga Alexeeva, Lavezza Zanders, Veronique King

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsMontreal Cognitive AssessmentDementiaNeuropsychologyMemory clinicCognitionCognitive impairmentGerontologyMedicinePsychologyPsychiatryAudiologyInternal medicineDisease

Abstract

fetched live from OpenAlex

The validity of the Montreal Cognitive Assessment (MoCA) as a screen for mild cognitive impairment (MCI) and dementia was evaluated in African Americans attending an urban outpatient memory disorders clinic. Eighty one patients ≥50 years old were administered the MoCA and neuropsychological tests. Clinicians, blinded to the MoCA scores, reviewed the neuropsychological findings and reports of instrumental activities of daily living and they assigned a diagnosis of normal cognition (NC; N = 16), MCI (N = 38), or dementia (N = 27). The MoCA scores of the 3 groups were significantly different (NC > MCI > dementia). Using cutoff scores of ≤24 points for MCI and ≤22 points for dementia, the MoCA had .95 sensitivity and .63 specificity for MCI and .96 sensitivity and .88 specificity for dementia. The MoCA is a valid and cost-effective screen for cognitive impairment in African Americans but with a higher likelihood of falsely classifying persons with NC as having MCI.

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 score0.014
Threshold uncertainty score0.450

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 head0.018
GPT teacher head0.324
Teacher spread0.306 · 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

Citations83
Published2014
Admission routes1
Has abstractyes

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