Longitudinal measurement properties of the Montreal Cognitive Assessment
Bibliographic record
Abstract
INTRODUCTION: The Montreal Cognitive Assessment (MoCA) has started to be used in longitudinal investigations to measure cognition trends but its measurement properties over time are largely unknown. This study aimed to examine the longitudinal measurement invariance of individual MoCA items. METHOD: We used four waves of data collected between 2014 and 2017 from a cohort study on health and well-being of older adults from twelve public housing estates in Hong Kong. We identified people aged 65 years or older at baseline who answered the MoCA items across all time points and had a valid indicator of educational level. A total of 1028 participants were included. We applied confirmatory factor analysis of ordinal variables to examine measurement invariance of the Chinese (Cantonese) MoCA (version 7.0) items across four time points, stratified by educational level, where invariant items were identified by sequential model comparisons. RESULTS: Four items exhibited a lack of measurement invariance across the four time points in both education groups (Clock Hand, abstraction, Delayed Recall, and Orientation). The items Cube and Sentence Repetition lacked longitudinal measurement invariance only in the "some education" group and the items Clock Shape and Clock Number only in the "no education" group. However, accounting for the lack of measurement invariance did not substantially affect classification properties for major neurocognitive disorder and mild cognitive impairment. CONCLUSIONS: Our findings support using MoCA to assess changes in cognition over time in the study population while calling for future research in other populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".