Multilingualism (But Not Always Bilingualism) Delays the Onset of Alzheimer Disease: Evidence From a Bilingual Community
Bibliographic record
Abstract
A recent paper by Bialystok et al in Neuropsychologia (vol. 45, pgs. 459 to 464) suggested that early bilingualism produced a statistically significant 4.1-year delay in onset of memory loss symptoms in older individuals with Alzheimer disease, possibly reflecting an increase in the cognitive reserve of these individuals. That study focused on multilingual elderly patients of whom 90% were immigrants. Our memory clinic, in Montreal Canada, has the advantage of having a large set of individuals who are either multilingual immigrants to Canada, or who are nonimmigrants but raised in both official languages of Canada--French and English. We thus attempted to replicate the above findings using a larger cohort in a different setting. We examined age at diagnosis of Alzheimer disease and age at symptom onset for all unilingual versus multilingual participants, and then for those who were nonimmigrant English/French bilinguals. Overall, we found a small but significant protective effect of more than 2 languages spoken, but we found no significant benefit in bilinguals overall in relation to age at diagnosis or age at symptom onset. However, in the immigrant group, the results mirrored those of Bialystok et al with 2 or more languages delaying the diagnosis of Alzheimer disease by almost 5 years. A trend toward the same effect was also seen in nonimmigrants whose first language was French. In contrast, in nonimmigrants whose first language was English, no such effect was found. These results are discussed in relation to the earlier findings and the theory of cognitive reserve.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".