Trajectories of Personality Traits Preceding Dementia Diagnosis
Bibliographic record
Abstract
BACKGROUND: Several retrospective studies using informant report have shown that individuals with dementia demonstrate considerable personality change. Two prospective studies, also using informant report, have shown that individuals who develop dementia show some personality changes prior to diagnosis. The current study is the first to assess personality trait change prior to dementia diagnosis using self-report measures from longitudinal data. METHOD: This study used data from the Swedish OCTO-Twin Study, a longitudinal panel of 702 twins aged 80 and older. Analysis was restricted to 86 individuals who completed the Eysenck Personality Inventory and received a dementia diagnosis during follow-up occasions. Latent growth curve analyses were used to examine trajectories of extraversion and neuroticism preceding dementia diagnosis. RESULTS: Controlling for sex, age, education, depressive symptoms, and the interaction between age and education, growth curve analyses revealed a linear increase in neuroticism and stability in extraversion. Individuals who were eventually diagnosed with dementia showed a significant increase in neuroticism preceding diagnosis of dementia. DISCUSSION: Personality change, specifically an increase in neuroticism, may be an early indicator of dementia. Identification of early indicators of dementia may facilitate development of screening assessments and aid in early care strategies and planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| 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".