Psychometrics of the Montreal Cognitive Assessment (MoCA) and its subscales: validation of the Taiwanese version of the MoCA and an item response theory analysis
Bibliographic record
Abstract
BACKGROUND: The Montreal Cognitive Assessment (MoCA) is an instrument for screening mild cognitive impairment (MCI). This study examined the psychometric properties and the validity of the Taiwan version of the MoCA (MoCA-T) in an elderly outpatient population. METHODS: Participants completed the MoCA-T, Mini-Mental State Examination (MMSE), and the Chinese Version Verbal Learning Test. The diagnosis of Alzheimer's disease (AD) was made based on the NINCDS-ADRDA criteria, and MCI was diagnosed through the criteria proposed by Petersen et al. (2001). RESULTS: Data were collected from 207 participants (115 males/92 females, mean age: 77.3 ± 7.5 years). Ninety-eight participants were diagnosed with AD, 71 with MCI, and 38 were normal controls. The area under the receiver operator curves (AUC) for predicting AD was 0.98 (95% confidence interval [CI] = 0.97-1.00) for the MMSE, and 0.99 (95% CI = 0.98-1.00) for the MoCA-T. The AUC for predicting MCI was 0.81 (95% CI = 0.72-0.89) using the MMSE and 0.91 (95% CI = 0.86-1.00) using the MoCA-T. Using an optimal cut-off score of 23/24, the MoCA-T had a sensitivity of 92% and specificity of 78% for MCI. Item response theory analysis indicated that the level of information provided by each subtest of the MoCA-T was consistent. The frontal and language subscales provided higher discriminating power than the other subscales in the detection of MCI. CONCLUSION: Compared to the MMSE, the MoCA-T provides better psychometric properties in the detection of MCI. The utility of the MoCA-T is optimal in mild to moderate cognitive dysfunction.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".