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Investigating the Measurement Precision of the Montreal Cognitive Assessment (MoCA) for Cognitive Screening in Parkinson's Disease Through Item Response Theory

2024· preprint· en· W4405716080 on OpenAlexaboutno aff
Pedro Renato de Paula Brandão, Danilo Assis Pereira, Brenda Hanae Bentes Koshimoto, Vanderci Borges, Henrique Ballalai Ferraz, Artur Francisco Schumacher Schuh, Carlos Roberto de Mello Rieder, Maira Rozenfeld Olchik, Ignácio F. Mata, Vítor Tumas, Bruno Lopes Santos‐Lobato

Bibliographic record

VenuePreprints.org · 2024
Typepreprint
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentDifferential item functioningItem response theoryPsychologyCognitionCohortParkinson's diseaseDiseasePsychometricsClinical psychologyGerontologyMedicineCognitive impairmentPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Background: The Montreal Cognitive Assessment (MoCA) is widely used to evaluate global cognitive function in older Brazilian adults. However, concerns persist regarding its applicability in non-homogeneous socio-demographic groups. This study scrutinizes the Brazilian version of the MoCA, focusing on its measurement properties in a diverse cohort of patients with Parkinson's disease (PD). Purpose: This study examined the psychometric properties of the Brazilian Portuguese MoCA in a heterogeneous sample of PD patients using item response theory (IRT) methods. Material and Methods: In a multicenter cross-sectional study, 484 PD patients aged 26-90 years (mean ± SD, 59.9 ± 11.1 years), with disease durations ranging from 1 to 35 years (mean ± SD, 8.7 ± 5.4 years), underwent MoCA testing. IRT analyses, including the Graded Response Model, evaluated item parameters, such as difficulty (location) and discrimination. Differential item functioning was analyzed vis-à-vis age and education using multiple indicators multiple causes (MIMIC) modeling. Results: The MoCA exhibited essential unidimensionality and satisfactory model fit. Attention and naming demonstrated high discrimination. Orientation and naming items were less challenging. Multiple domains showed differential item functioning related to age and education, underscoring the necessity of considering background characteristics when interpreting total scores. Conclusion: This study enriches validity evidence for the MoCA in PD by providing a detailed analysis of its measurement properties and sources of score bias. Tailoring test content and norms based on education and establishing computerized scoring algorithms leveraging item parameters may optimize the tool’s reliability and fairness. Refinements to mitigate differential item functioning could enable precise cognitive screening across diverse socio-demographic backgrounds.

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)0.050
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.114
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.385
Teacher spread0.229 · 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 designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

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