LONGITUDINAL CHANGE IN PERFORMANCE ON THE MONTREAL COGNITIVE ASSESSMENT IN THE PEOPLE WITH DEMENTIA
Bibliographic record
Abstract
Abstract The Montreal Cognitive Assessment (MoCA), a widely employed cognitive screening tool, is designed to identify mild cognitive impairment and diverse forms of dementia. Through its multidimensional evaluation of cognitive domains, MoCA supports clinicians in precise assessment and intervention planning for individuals manifesting cognitive deficits. Despite MoCA’s prevalence, limited research has explored its temporal dynamics in individuals experiencing memory loss, particularly across different stages of dementia. This study examined the MoCA changes of a longitudinal study analyzing the conversational speech in 8 older adults (5 males 3 females) with mild- to moderate-stage of dementia. Repeated biweekly measures at 12 intervals over six months were employed to gauge changes in total MoCA scores and its sub-tasks. Our findings reveal: 1) a significant negative correlation (r=-0.22, p=0.03) between the total MoCA score and the total time spent in tests; 2) most participants present a positive correlation (r=0.03 to 0.6) in their first memory trial session and a negative correlation (r=-0.1 to -0.8)in the second trial between words recalled and the time spent; 3) over 60% of participants demonstrating a declining trend in naming sessions, while orientation sessions exhibit no clear trend. These outcomes imply MoCA’s susceptibility to a “learning effect” with repeated measurements in people with dementia. Future longitudinal studies should consider alternate MoCA versions or extended assessment intervals to mitigate this effect.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".