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Record W2133982308 · doi:10.1177/0891988710390813

Validity of the Montreal Cognitive Assessment (MoCA) as a Screening Test for Mild Cognitive Impairment (MCI) in a Cardiovascular Population

2010· article· en· W2133982308 on OpenAlexaboutno aff
Skye N. McLennan, Jane L. Mathias, Lucy Brennan, Simon Stewart

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2010
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitive impairmentCognitionGerontologyTest (biology)PopulationDementiaPsychologyMedicineAudiologyPsychiatryDiseaseInternal medicine

Abstract

fetched live from OpenAlex

While rates of mild cognitive impairment (MCI) are relatively high in populations with cardiovascular diseases and risk factors, screening tests for MCI have not been evaluated in this patient group. This study investigated the sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) tool for detecting MCI in 110 patients (mean age 67.9 + 11.7 years; 60% female) recruited from hospital cardiovascular outpatient clinics. Mean MoCA performance was relatively low (22.8 + 3.8) in this group, with 72.1% of participants scoring below the recommended cutoff for cognitive impairment (<26). The presence of MCI was determined using the Neuropsychological Assessment Battery Screening Module (NAB-SM). Both amnestic MCI and multiple-domain MCI were identified. The optimum MoCA cutoff for detecting MCI in this group was <24. At this cutoff, the MoCA's sensitivity for detecting amnestic MCI was 100% and for multiple-domain MCI it was 83.3%. Specificity rates for amnestic MCI and multiple-domain MCI were 50.0% and 52% respectively. The poor specificity of the MoCA suggests that it will have limited value as a screening test for MCI in settings where the overall prevalence of MCI is low.

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 score0.009
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.319
Teacher spread0.302 · 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

Citations196
Published2010
Admission routes1
Has abstractyes

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