Confirmatory factor analysis of the Montreal Cognitive Assessment in evaluating elderly mild cognitive impairment
Bibliographic record
Abstract
Objective To assess the psychometric potential of the Montreal Cognitive Assessment Scale-Beijing (MoCA-BJ) as a screening instrument for mild cognitive impairment (MCI) in older adults in Wuhan communities of central China. Methods MoCA-BJ and Mini-Mental State Examination (MMSE) were adopted to assess the MCI of 381 older adults from 13 communities in Wuhan in 2015. Confirmatory factor analysis was conducted to evaluate the construct validity of MoCA-BJ, and the relationship between all aspects of cognitive function and MoCA different dimensions. Results MoCA-BJ had acceptable reliability (w=0.76), and MoCA-BJ and MMSE estimation results were highly correlated (r=0.73, P<0.01). By comparing three measurement models through confirmatory factor analysis, we found that the MoCA-BJ scale had two factors (F1: visual space executive function, F2: memory-based other cognitive functions) in model 3, fit degree of which was higher than model 1 by one factor, and there was a statistically significant difference in the number of factors between model 1 and model 3 (χ2dif=8.73, P<0.01). Conclusions The MoCA-BJ has two underlying factors that respectively represent two highly correlated but distinct factors, cognition and visual-spatial. Uninformative items should be revised with culturally sensitive items and the cut-off point for mild impairment should also be altered. Key words: Cognition disorders; Factor analysis, statistical; Montreal Cognitive Assessment Scale
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".