One‐Year Change in the Japanese Version of the Montreal Cognitive Assessment Performance and Related Predictors in Community‐Dwelling Older Adults
Bibliographic record
Abstract
OBJECTIVES: To examine the distribution and associated predictors of 1-year changes in the Japanese version of the Montreal Cognitive Assessment (MoCA-J) in community-dwelling older adults. DESIGN: Prospective cohort study. SETTING: Population-based cohort study in Tokyo, Japan. PARTICIPANTS: Individuals aged 65 to 84 (N = 496). MEASUREMENTS: Multinomial logistic regression analysis was performed to estimate the odds of experiencing subsequent improvement in MoCA-J performance, as opposed to stable or deteriorating, while simultaneously adjusting for baseline MoCA-J score and major confounders. RESULTS: Mean age was 74.0 ± 4.8; mean MoCA-J score was 23.7 ± 3.6. Only 40% had stable MoCA-J performance; 30% experienced deterioration and 30% improvement. Age increment, hospitalization in previous year, slower Timed Up and Go (TUG) score, and slower maximum walking speed were predictive of subsequent MoCA-J performance deterioration. CONCLUSION: Slower TUG and walking speed performances were independent predictors of short-term MoCA-J deterioration. Research aimed at assessing lower-extremity performance-based tests in MCI-related decision-making is warranted.
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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 | 0.002 |
| 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 | 0.001 |
| 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".