Brief Screening for Mild Cognitive Impairment in Subcortical Ischemic Vascular Disease: A Comparison Study of the Montreal Cognitive Assessment with the Mini-Mental State Examination
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To assess the validity of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) in the detection of vascular mild cognitive impairment (VaMCI) in patients with subcortical ischemic vascular disease (SIVD). METHODS: Among 102 SIVD patients, both cutoff scores of the MMSE and MoCA for differentiating VaMCI from no cognitive impairment (NCI) or differentiating VaMCI from vascular dementia (VaD) were determined by the receiver operator characteristic (ROC) analysis. Optimal sensitivity with specificity of cutoff scores was obtained after the raw scores were adjusted for education. RESULTS: After adjusting for education, the MoCA cutoff score for differentiating VaMCI from NCI was at 24/25 and that for differentiating VaMCI from VaD was at 18/19. After applying the adjusted MoCA scores from 19 to 24 to identify VaMCI in all SIVD patients, sensitivity was at 76.7% and specificity was at 81.4% (κ = 0.579). The adjusted cutoff score of the MMSE for differentiating VaMCI from NCI was at 28/29 and that for differentiating VaMCI from VaD was at 25/26. The sensitivity and specificity of the adjusted MMSE was at 58.1 and 71.2%, respectively, when using the score from 26 to 28 to identify VaMCI in SIVD patients (κ = 0.294). CONCLUSIONS: The MoCA detected subcortical VaMCI better than the MMSE.
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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 | 0.012 | 0.024 |
| 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 | 0.001 |
| 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".