PREMORBID PERSONALITY AND THE OCCURRENCES OF THE RISK OF MCI AFTER 3 YEARS IN JAPANESE ELDERLY
Bibliographic record
Abstract
Background: Recent studies have reported that the onset of dementia or mild cognitive impairment (MCI) in old age has associations with personalities of individuals before their onsets. However, it had not been confirmed in Japanese elderly people. Objective: To investigate the associations between personality traits and MCI risks by the prospective study on Japanese elderly people who are cognitively intact. Methods: Data were obtained in two waves three years apart from 1251 community-dwelling older adults (637 women, mean age=74.6 ± 4.9 years at Wave 1). Cognitive function and 5 big five personality traits were measured at Wave 1 by Japanese version of Montreal Cognitive Assessment (Moca-J) and NEO Five Factor inventory. After 3 years, Moca-J was conducted at Wave 2. Result: The score of Moca-J less than 26 was judged to have MCI risk. 325 individuals had no MCI risk at Wave 1. Three years later, 147 of 325 individuals were with risk of MCI at Wave 2. As a result of a binary logistic regression analysis with whether having MCI risk or not at Wave 2 as a objective variable, it was indicated that the occurrences MCI risk in three years significantly associated with high neuroticism(OR=2.11, 95%CL:1.31–3.40,p<01), and with low openness(OR=.50, 95%CL:.31-.86, p<.01) at Wave 1. There were no significant associations between other personality traits and MCI risks. Discussion: These results suggested that, as reported by preceding studies, the associations between personality traits (neuroticism and openness) and MCI risks in three years on Japanese elderly people.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".