A subtest analysis of the Montreal cognitive assessment (MoCA): which subtests can best discriminate between healthy controls, mild cognitive impairment and Alzheimer's disease?
Bibliographic record
Abstract
BACKGROUND: It is necessary to continue to explore the psychometric characteristics of key cognitive screening tests such as the Montreal Cognitive Assessment (MoCA) to diagnose cognitive decline as early as possible and to attend to the growing need of clinical trials involving mild cognitive impairment (MCI) participants. The main aim of this study was to assess which MoCA subtests could best discriminate between healthy controls (HC), participants with MCI, and Alzheimer's disease (AD). METHODS: Cross-sectional analysis of 136 elderly with more than four years of education. All participants were submitted to detailed clinical, laboratory, and neuroimaging evaluation. The MoCA, Mini-Mental State Examination (MMSE), the Cambridge Cognitive Examination (CAMCOG), Geriatric Depression Scale (GDS), and Functional Activities Questionnaire (FAQ) were applied to all participants. The MoCA test was not used in the diagnostic procedure. RESULTS: Median MoCA total scores were 27, 23 and 18 for HC, MCI, and AD, respectively (p < 0.001). Word repetition, inverse digits, serial 7, phrases, verbal fluency, abstraction, and word recall discriminated between MCI and HC participants (p < 0.001). The clock drawing, the rhino naming, delayed recall of five words and orientation discriminated between patients with MCI and AD (p < 0.001). A reduced version of the MoCA with only these items did not improve accuracy between MCI and HC (p = 0.076) or MCI and AD (p = 0.119). CONCLUSIONS: Not all MoCA subtests might be fundamental to clinical diagnosis of MCI. The reduced versions of MoCA did not add diagnostic accuracy.
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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 | 0.001 | 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) | 0.000 | 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".