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Record W3006114524 · doi:10.1016/j.prdoa.2020.100042

Differences in performance on English and Hebrew versions of the MoCA in Parkinson's patients

2020· article· en· W3006114524 on OpenAlexaboutno aff
Yaqian Xu, Anat Mirelman, Rachel Saunders‐Pullman, Helen Mejia‐Santana, Elise Caccappolo, Deborah Raymond, Nir Giladi, Susan Bressman, Karen Marder, Roy N. Alcalay

Bibliographic record

VenueClinical Parkinsonism & Related Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeNational Institutes of HealthMichael J. Fox Foundation for Parkinson's Research
KeywordsHebrewMontreal Cognitive AssessmentComparabilityRating scalePsychologyCognitionLinguisticsMedicinePsychiatryMathematicsDevelopmental psychologyCognitive impairmentPhilosophy

Abstract

fetched live from OpenAlex

The Montreal Cognitive Assessment (MoCA), an instrument widely used for cognitive screening in Parkinson's disease (PD), is validated in Hebrew and English. However, it remains unknown whether the scores are comparable. The MoCA was analyzed in 483 Ashkenazi Jewish PD patients in Tel-Aviv and New York who had MoCA ≥21. Each section of the MoCA was compared between English and Hebrew. Linear regression models were used to test the association between MoCA performance and language. Total MoCA scores were lower in Hebrew than in English (25.4 versus 26.1; P = 0.007), even after adjustment for age, sex, PD duration, genotype, levodopa equivalent dose, the Unified Parkinson's Disease Rating Scale (UPDRS-III), and Geriatric Depression Scale score in a linear model (P < 0.001). However, when language sections were removed from the total, scores were similar between the languages (Hebrew 23.7 versus English 23.4, P = 0.111). The language section of the MoCA may be more difficult in Hebrew. The comparability of MoCA in different languages requires further evaluation.

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 score0.029
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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