TWO-YEAR CHANGE IN MONTREAL COGNITIVE ASSESSMENT AND RELATED PREDICTORS IN COMMUNITY-DWELLING ELDERLY
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is primarily used for mild cognitive impairment (MCI) screening in older adults in the clinical setting. The epidemiology of MoCA performance in population-based settings remains to be better characterized. The goal of this study was to assess short-term changes in MoCA scores, and examine selected risk factors, with emphasis on the evaluation of the a priori hypotheses of engagement in volunteer activities as a protective factor against cognitive deterioration. Data of the prospective study were from 438 community-dwelling older Japanese (age range: 65–84) living in an urban area in Tokyo (2013–2015). Outcome was short-term cognitive deterioration, defined as decline of 2 or more points in MoCA-J scores obtained 2 years apart. Multivariate logistic regression was used with adjustment for age, gender, chronic conditions, self-rated health, baseline MoCA-J, and recent hospitalization. Analytic sample had mean age of 73.3 ± 5.4 years old; and mean MoCA-J of 24.4 ± 3.8; 58.2% were female. Of study participants, 38.1% experienced cognitive deterioration. Engagement in volunteer activities was associated with lower adjusted odds of subsequent MoCA-J deterioration 2 years later (odds ratio [OR]: 0.32; 95% confidence interval [CI]: 0.100–0.98); contrastingly, going out of the home less than once/day (OR: 2.90; 95% CI: 1.24–6.80), and slower timed Up and Go (OR: 1.28; 95% CI: 1.00–1.60 per 1 second slower) were risk factors for cognitive deterioration In conclusion, engagement in volunteer activity was an independent protective factor against cognitive deterioration, while homebound status and worse mobility were risk factors of short-term MoCA-J deterioration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".