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 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 | 0.030 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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".