Montreal cognitive assessment in assessing clinical severity and white matter hyperintensity in Alzheimer's disease with normal control comparison.
Bibliographic record
Abstract
PURPOSE: Use Taiwanese version of the Montreal Cognitive Assessment (MoCA) in evaluating patients in different stages of Alzheimer's disease (AD) and correlate with white matter change. METHODS: Ninety-seven normal controls (NC), 52 very-mild AD (clinical dementia rating [CDR] = 0.5), 48 mild AD (CDR = 1) and 38 moderate AD (CDR = 2) patients were enrolled for the MoCA, Mini- Mental State Examination (MMSE) and the Cognitive Assessment Screening Instrument (CASI). White matter hyperintensities (WMHs) on brain MRI were visually rated and classified as deep or periventricular WMHs. RESULTS: In NC group, education (β = 0.326) but not age (β = -0.183, p = 0.069), was significantly related to MoCA score. However, while we added two points to the AD patients with less than 6 years education, the effects of education disappeared as compared with those of 7 years of education. For all educational levels, the cutoff value of MoCA for very-mild AD was 22/23 (sensitivity = 82.7%, specificity = 87.6%). No significant differences were found in the areas under the curves that differentiated NC from the patients with AD for MoCA and MMSE (differences = 0.008, p = 0.490), or for MoCA and CASI (differences = 0.023, p = 0.082). Total WMHs, frontal deep and periventricular WMHs were inversely correlated with the attention and delayed-recall subdomain. CONCLUSION: The MoCA is a good clinical tool for screening very-mild stage AD if the educational effects are carefully considered. The correlation between the executive subdomains with the frontal WMHs also makes it a useful tool for detecting subtle WMHs.
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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 | 0.003 | 0.004 |
| 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) | 0.001 | 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".