P1‐401: The Montreal cognitive assessment (MoCA) is superior to the mini‐mental state examination (MMSE) in detecting patient's with moderate cognitive impairment, no‐dementia (CIND) and at high risk of dementia
Bibliographic record
Abstract
The MMSE has been found to be less sensitive than the MoCA in screening for Mild Cognitive Impairment and mild dementia. However, the MoCA has not been evaluated as a cognitive screening tool in Singapore. We aimed to validate the Singaporean version of the MoCA and compare it to the MMSE in a memory clinic setting for identifying patients with or at high risk of dementia. The MMSE and MoCA were administered to consecutive patients attending the National University Health System Memory Clinics (N = 316) over a 2 year period. Dementia diagnosis was established by formal neuropsychological evaluation using the DSM-IV criteria. Patients were classified into 4 groups: No Cognitive Impairment (NCI), CIND-mild (≤2 cognitive domains impaired), CIND-moderate (>2 cognitive domains impaired) and dementia, using previously validated methods. The majority of patients were Chinese (87%) female (53%) with mean age of 73±10 years and a low level of education (6±5 years). There were 60 (19%) NCI, 45 (14%) CIND-mild, 39 (12%) CIND-moderate and 172 (54%) with dementia. The mean MoCA scores were 24.3±3.4, 20.7±4.5, 15.4±3.6 and 10.1±4.7 respectively. Post hoc analysis showed that MoCA total scores could significantly differentiate between all 4 groups. Receiver operator curve (ROC) analysis established the optimal cutoff scores of the MoCA at ≤18 (AUC 0.80, Sensitivity 94%, Specificity 66%, PPV 77%, NPV 91%, correctly classified 81%) in differentiating between demented and non-demented patients. The mean MMSE scores for NCI, CIND mild, CIND moderate and dementia were 27.1±2.3, 24.4±3.2, 20.5±3.9 and 15.1±5.1 respectively, and the MMSE (≤22) showed similar discriminant indices for dementia. Among the non-demented patients, MoCA at a cutoff of ≤18 (AUC 0.88, Sensitivity 90%, Specificity 87%, PPV 71%, NPV 96%, correctly classified 88%) could differentiate between the cognitively normal / low-risk (NCI+CIND-mild) and patients at-risk for dementia (CIND-moderate). By comparison, the MMSE (≤22) was less sensitive (p = 0.01) in identifying such patients (AUC 0.725, Sensitivity 64.1%, Specificity 81%, PPV 55.6%, NPV 85.9%, correctly classified 76.4%). The MoCA is sensitive in identifying Singaporean patients with dementia and is superior to the MMSE in screening for patients at high risk for dementia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".