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Record W1833922410 · doi:10.1016/j.dadm.2015.05.002

Concordance of the Montreal cognitive assessment with standard neuropsychological measures

2015· article· en· W1833922410 on OpenAlexaboutno aff
Sally J. Vogel, Sarah J. Banks, Jeffrey L. Cummings, Justin B. Miller

Bibliographic record

VenueAlzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentNeuropsychologyConcordanceVarimax rotationNeuropsychological assessmentPsychologyCognitionClinical psychologyPsychiatryPsychometricsMedicineCognitive impairmentCronbach's alpha

Abstract

fetched live from OpenAlex

INTRODUCTION: The concordance of the Montreal cognitive assessment (MoCA) with more comprehensive neuropsychological measures remains unclear. This study examined the individual MoCA domains with more comprehensive and commonly used neuropsychological measures to determine the degree of overlap. METHODS: Data included individuals seen in an outpatient neurology clinic specializing in neurodegenerative disease who were administered the MoCA and also underwent neuropsychological assessment (n = 471). A principal component analysis with varimax rotation was completed using the MoCA domain scores and comprehensive neuropsychological evaluation measures. RESULTS: Four factors emerged accounting for 55.6% of the variance: (1) visuospatial/executive functioning; (2) memory; (3) attention; and (4) language. The individual MoCA domain scores demonstrated high factor loadings with standard neuropsychological measures purported to measure similar cognitive constructs. DISCUSSION: These findings provide empirical validation for the MoCA domain classifications, lending further support for the use of the MoCA as a cognitive screen that reflects similar constructs as those measured by a comprehensive battery.

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 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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.056
GPT teacher head0.377
Teacher spread0.320 · 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

Citations78
Published2015
Admission routes1
Has abstractyes

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