Past exposure to vaccines and subsequent risk of Alzheimer's disease.
Bibliographic record
Abstract
BACKGROUND: It has been suggested that changes to the immune system could be a factor in age-related conditions such as Alzheimer's disease. Our objective was to examine the association between past exposure to conventional vaccines and risk of Alzheimer's disease. METHODS: We analyzed data from a representative community sample of subjects 65 years of age or older participating in the Canadian Study of Health and Aging, a prospective cohort study of dementia. Screening and clinical evaluations were done at both baseline and follow-up. Past exposure to vaccines was assessed at baseline by means of a self-administered questionnaire. RESULTS: Of the 4392 eligible subjects who were cognitively unimpaired and for whom vaccine information was available at baseline (in 1991-1992) and who completed follow-up 5 years later (in 1996-1997), 527 were diagnosed as having cognitive impairment or dementia other than Alzheimer's disease and were excluded from these analyses. Of the remaining subjects, 3682 were cognitively unimpaired at follow-up and 183 were newly diagnosed as having Alzheimer's disease. After adjustment for age, sex and education, past exposure to vaccines against diphtheria or tetanus, poliomyelitis and influenza was associated with lower risk for Alzheimer's disease (odds ratio [OR] 0.41, 95% confidence interval [CI] 0.27-0.62; OR 0.60, 95% CI 0.37-0.99; and OR 0.75, 95% CI 0.54-1.04 respectively) than no exposure to these vaccines. INTERPRETATION: Past exposure to vaccines against diphtheria or tetanus, poliomyelitis and influenza may protect against subsequent development of Alzheimer's disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".