Premorbid personality traits as predictors for incident predementia syndromes: a multistate model approach
Bibliographic record
Abstract
OBJECTIVE: Associations have been found between five-factor model (FFM) personality traits and risk of developing specific predementia syndromes such as subjective cognitive decline (SCD) and mild cognitive impairment (MCI). The aims of this study were to: 1) Compare baseline FFM traits between participants who transitioned from healthy cognition or SCD to amnestic MCI (aMCI) versus non-amnestic MCI (naMCI); and 2) Determine the relationship between FFM traits and risk of transition between predementia cognitive states. METHODS: Participants were 562 older adults from the Einstein Aging Study, 378 of which had at least one follow-up assessment. Baseline data collected included levels of FFM personality traits, anxiety and depressive symptoms, medical history, performance on a cognitive battery, and demographics. Follow-up cognitive diagnoses were also recorded. RESULTS: Mann-Whitney U tests revealed no differences in baseline levels of FFM personality traits between participants who developed aMCI compared to those who developed naMCI. A four-state multistate Markov model revealed that higher levels of conscientiousness were protective against developing SCD while higher levels of neuroticism resulted in an increased risk of developing SCD. Further, higher levels of extraversion were protective against developing naMCI. CONCLUSIONS: FFM personality traits may be useful in improving predictions of who is at greatest risk for developing specific predementia syndromes. Information on these personality traits could enrich clinical trials by permitting trials to target individuals who are at greatest risk for developing specific forms of cognitive impairment. These results should be replicated in future studies with larger sample sizes and younger participants.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".