The Association Between Emotional Expressivity in Autobiographies from Early Adulthood and the Risk of Dementia in the Context of Written Language Skills
Bibliographic record
Abstract
Background: Risk factors for dementia, such as Alzheimer's disease, are complex and span a lifetime. Exploring novel factors, such as characteristics of writing, may provide insight into dementia risk. Objective: To investigate the association between emotional expressivity and risk of dementia in the context of a previously identified risk factor, written language skills. Methods: The Nun Study recruited 678 religious sisters aged 75 + years. Of these, 149 U.S.-born participants had archived autobiographies handwritten at a mean age of 22 years. The autobiographies were scored for frequency of emotion word usage and language skills (e.g., idea density). The association of emotional expressivity and a four-level composite variable (combining high/low emotional expressivity and high/low idea density) with dementia was assessed using logistic regression models adjusted for age, education, and apolipoprotein E. Results: Within the composite variable, odds of dementia increased incrementally, with opposing effects of emotional expressivity across the two idea density levels. Compared to the referent category (low emotional expressivity/high idea density), the risk of dementia increased in those with high emotional expressivity/high idea density (OR = 2.73, 95% CI = 1.05-7.08), while those with low emotional expressivity/low idea density had the highest risk (OR = 18.58, 95% CI = 4.01-86.09). Conclusion: Dementia risk is better captured by inclusion of multiple measures relating to characteristics of writing. Emotional expressivity may be protective when individuals are at increased risk due to poor written language skills (i.e., low idea density), but detrimental when not at risk (i.e., high idea density). Our findings indicate that emotional expressivity is a contextually-dependent novel risk factor for dementia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".