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Record W4391630772 · doi:10.1097/mrr.0000000000000612

Reliability of the Montreal Cognitive Assessment in people with stroke

2024· article· en· W4391630772 on OpenAlexaboutno aff
H. T. Lau, Yi-hung Lin, Keh‐chung Lin, Yi-Chun Li, Grace Yao, Chih-yu Lin, Yi‐Hsuan Wu

Bibliographic record

VenueInternational Journal of Rehabilitation Research · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsIntraclass correlationMontreal Cognitive AssessmentConfidence intervalReliability (semiconductor)Limits of agreementStandard errorStroke (engine)MedicinePsychologyPhysical therapyCognitive impairmentStatisticsAudiologyCognitionMathematicsPsychometricsNuclear medicinePsychiatry

Abstract

fetched live from OpenAlex

This study examined the relative and absolute reliability of the Taiwanese version of the MoCA (MoCA-T) in people with stroke. The study recruited 114 individuals who were at least 3 months after the onset of a first-ever unilateral stroke. The MoCA-T was administered twice, at a 6-week interval, to all participants. The relative reliability was assessed using the intraclass correlation coefficient (ICC), and the absolute reliability was assessed using standard error of measurement (SEM), the smallest real difference (SRD), the SRD percentage, and the Bland-Altman method. The ICC analysis showed the MoCA-T was highly reliable (ICC = 0.85). The absolute reliability was between an acceptable and excellent level, where the SEM and the SRD at the 95% confidence interval were 1.38 and 3.83, respectively. The Bland-Altman analyses showed no systematic bias between repeated measurements. The range of the 95% limits of agreement was narrow, indicating a high level of stability over time. These findings suggest that the MoCA-T has high agreement between repeated measurements without systematic bias. The threshold to detect real change stands between an acceptable and excellent level. The MoCA-T is a reliable tool for cognitive screening in stroke rehabilitation.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.421
Teacher spread0.403 · 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

Citations11
Published2024
Admission routes1
Has abstractyes

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