The Montreal Cognitive Assessment is superior to the Mini–Mental State Examination in detecting patients at higher risk of dementia
Bibliographic record
Abstract
BACKGROUND: To examine the discriminant validity of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) in detecting patients with cognitive impairment at higher risk for dementia at a memory clinic setting. METHODS: Memory clinic patients were administered the MoCA, MMSE, and a comprehensive formal neuropsychological battery. Mild cognitive impairment (MCI) subtypes were dichotomized into two groups: single domain-MCI (sd-MCI) and multiple domain-MCI (md-MCI). Area under the receiver operating characteristic curve (ROC) analysis was used to compare the discriminatory ability of the MoCA and the MMSE. RESULTS: Two hundred thirty patients were recruited, of which 136 (59.1%) were diagnosed with dementia, 61 (26.5%) with MCI, and 33 (14.3%) with no cognitive impairment (NCI). The majority of MCI patients had md-MCI (n = 36, 59%). The MoCA had significantly larger AUCs than the MMSE in discriminating md-MCI from the lower risk group for incident dementia (NCI and sd-MCI) [MoCA 0.92 (95% CI, 0.86-0.98) vs. MMSE 0.84 (95% CI, 0.75-0.92), p = 0.02). At their optimal cut-off points, the MoCA (19/20) remained superior to the MMSE (23/24) in detecting md-MCI [sensitivity: 0.83 vs. 0.72; specificity: 0.86 vs. 0.83; PPV: 0.79 vs. 0.72; NPV: 0.89 vs. 0.83; correctly classified: 85.1% vs. 78.7%]. CONCLUSION: The MoCA is superior to the MMSE in the detection of patients with cognitive impairment at higher risk for incident dementia at a memory clinic setting.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".