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Record W2117034021 · doi:10.1177/0891988712457047

Validation of the Hebrew Version of the MoCA Test as a Screening Instrument for the Early Detection of Mild Cognitive Impairment in Elderly Individuals

2012· article· en· W2117034021 on OpenAlexaboutno aff
Michal Lifshitz, Tzvi Dwolatzky, Yan Press

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2012
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentHebrewCognitionAsymptomaticAudiologyPopulationTest (biology)DementiaPsychologyMedicineCognitive impairmentPsychiatryClinical psychologyGerontologyInternal medicineDisease

Abstract

fetched live from OpenAlex

INTRODUCTION: The English version of the Montreal Cognitive Assessment (MoCA) test has been shown to be reliable in screening for mild cognitive impairment (MCI). However, the sensitivity and specificity of the Hebrew version of this instrument are yet to be determined. METHODS: The study population consisted of 2 groups of older individuals, 74 patients diagnosed with MCI and 80 patients who were cognitively asymptomatic. Cognitive evaluation included the Mini-Mental State Examination (MMSE), Mindstreams computerized cognitive assessment, and the MoCA test. RESULTS: The Hebrew version of MoCA distinguished between cognitively asymptomatic older individuals and those with MCI, with a sensitivity of 94.6% and a specificity of 76.3%, using a cutoff of 26/30 points. CONCLUSIONS: The Hebrew version of the MoCA test is effective for identifying MCI in older patients. As a screening instrument for MCI, its higher sensitivity makes it preferable o the MMSE, which is used extensively in the clinical setting.

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 score0.025
Threshold uncertainty score0.187

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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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

Citations74
Published2012
Admission routes1
Has abstractyes

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