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Record W2809146755 · doi:10.1177/1073191118778896

Determining a Short Form Montreal Cognitive Assessment (s-MoCA) Czech Version: Validity in Mild Cognitive Impairment Parkinson’s Disease and Cross-Cultural Comparison

2018· article· en· W2809146755 on OpenAlexaboutno aff
Ondřej Bezdíček, Markéta Červenková, Tyler M. Moore, Hana Georgi, Zdeněk Šulc, David A. Wolk, Daniel Weintraub, Paul J. Moberg, Robert Jech, Miloslav Kopeček, David R. Roalf

Bibliographic record

VenueAssessment · 2018
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institute on Aging
KeywordsMontreal Cognitive AssessmentPsychologyCognitionCognitive impairmentParkinson's diseaseCzechAudiologyPsychiatryDiseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

The Montreal Cognitive Assessment (MoCA) is one of the most common screening instruments for mild cognitive impairment. However, the standard MoCA is approximately two times longer to administer than the Mini-Mental State Examination. A total of 699 Czech and 175 American participants received the standard MoCA Czech and English versions and in the clinical part, a sample of 102 nondemented patients with Parkinson’s disease (PD). We created a validated Czech short version (s-MoCA-CZ) from the original using item response theory. As expected, s-MoCA-CZ scores were highly correlated with the standard version (Pearson r = .94, p < .001). s-MoCA-CZ also had 80% classification accuracy in the differentiation of PD mild cognitive impairment from PD without impairment. The s-MoCA-CZ, a brief screening tool, is shorter to administer than the standard MoCA. It provides high-classification accuracy for PD mild cognitive impairment and is equivalent to that of the standard MoCA-CZ.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.402
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2018
Admission routes1
Has abstractyes

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