Key Words Montreal Cognitive Assessment Clinical Dementia Rating Mild cognitive impairment
Bibliographic record
Abstract
Aims: To examine the effectiveness of the Montreal Cognitive Assessment (MoCA) to screen people with mild cognitive impairment (MCI), to associate the MoCA score with the presence of infarction, and to detect the characteristics of people with very mild subcortical vascular de-mentia (vmSVD). Methods: 392 out of 886 community dwellers aged 75 years and above living in Kurihara, Northern Japan, agreed to participate in our study; 164 scored a Clinical Dementia Rating (CDR) of 0 (healthy), 184 scored a CDR of 0.5 (MCI) and 44 scored a CDR of 1+ (dementia). The participants scoring a CDR of 0.5 were divided into 2 subtypes: 37 had vmSVD and 147 had other types of dementia. The objective variables were the total MoCA, the MoCA subscale and the Mini-Mental State Examination (MMSE). Results: There was a difference in the MoCA and MMSE scores between the 3 CDR groups. The MoCA score overlapped in participants with CDR 0 and 0.5. There were significant CDR effects, while there were no significant infarction effects for the MoCA and MMSE. vmSVD participants had lower scores on the total MoCA, the MoCA attention subscale and MMSE than healthy elderly people and participants with other types of dementia. Conclusion: Our results suggested that MMSE performed rather well and that the MoCA is not superior to MMSE in MCI and vmSVD participants aged 75 and above in a commu-nity. Copyright © 2012 S. Karger AG, Basel
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".