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Record W4413054548 · doi:10.1080/23279095.2025.2541812

Diagnostic validity of the Persian version of Montreal Cognitive Assessment – basic for cognitive screening

2025· article· en· W4413054548 on OpenAlexaffabout
Ahmad Reza Khatoonabadi, Amin Modarres Zadeh, Saman Maroufizadeh

Bibliographic record

VenueApplied Neuropsychology Adult · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsInstitute of Population and Public Health
Fundersnot available
KeywordsMontreal Cognitive AssessmentPersianNeuropsychologyCognitionCognitive impairmentGerontologyPopulationPsychologyNeuropsychological assessmentMedicineDementiaClinical psychologyAudiologyPsychiatryDiseaseInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The growing number of older people with Mild Cognitive Impairment (MCI) highlights the need for suitable and effective neuropsychological assessments. The Montreal Cognitive Assessment Basic (MoCA-B) is designed to identify MCI in individuals with lower literacy and education levels. This study seeks to validate the use of MoCA-B in the Persian-speaking population. METHODS: In this cross-sectional study, the original English version of the MoCA-B test was translated into Persian using the forward-backward method. The study involved 60 cognitively healthy aging individuals, 30 with Alzheimer's disease, and 30 MCI patients. All participants met the MMSE, MoCA-B, DSM-5, and Albert's criteria. RESULTS: MoCA-B scores in patients with AD were significantly lower than in the patients with MCI and healthy individuals (P < 0.001). They were significantly lower in MCI than individuals without cognitive impairment (P < 0.001). The cutoff score for discriminating between patients with AD/MCI and individuals without cognitive impairment was 20.5 (sensitivity = 95.0%, specificity = 88.3%, AUC = 0.972). CONCLUSION: This study shows that the MoCA-B is a suitable screening tool for distinguishing persons with cognitive impairment (MCI and AD) in the Persian-speaking population.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.090
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.020
GPT teacher head0.348
Teacher spread0.328 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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