Multilingualism and Dementia Risk: Longitudinal Analysis of the Nun Study
Bibliographic record
Abstract
BACKGROUND: Multilingualism is associated with enhanced executive function and may thus prevent cognitive decline and reduce the risk of dementia. OBJECTIVE: To determine whether multilingualism is associated with delayed onset or reduced risk of dementia. METHODS: Dementia was diagnosed in the Nun Study, a longitudinal study of religious sisters aged 75+ years. Multilingualism was self-reported. Dementia likelihood was determined in 325 participants using discrete-time survival analysis; sensitivity analyses (n = 106) incorporated additional linguistic measures (idea density and grammatical complexity). RESULTS: Multilingualism did not delay the onset of dementia. However, participants speaking four or more languages (but not two or three) were significantly less likely to develop dementia than monolinguals (OR = 0.13; 95% CI = 0.01, 0.65, adjusted for age, apolipoprotein E, and transition period). This significant protective effect of speaking four or more languages weakened (OR = 0.53; 95% CI = 0.06, 4.91) in the presence of idea density in models adjusted for education and apolipoprotein E. CONCLUSION: Linguistic ability broadly was a significant predictor of dementia, although it was written linguistic ability (specifically idea density) rather than multilingualism that was the strongest predictor. The impact of language on dementia may extend beyond number of languages spoken to encompass other indicators of linguistic ability. Further research to identify the characteristics of multilingualism most salient for risk of dementia could clarify the value, target audience, and design of interventions to promote multilingualism and other linguistic training as a strategy to reduce the risk of dementia and its individual and societal impacts.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| 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".