Influence of Premorbid IQ and Education on Progression of Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Lower education is associated with a higher risk of developing Alzheimer's disease (AD). Years of education and measures of general intellectual function (IQ) are highly correlated. It is important to determine whether there is a relationship between education and AD outcomes that is independent of IQ. OBJECTIVE: To test the hypothesis that premorbid IQ is a stronger predictor of cognitive decline, global progression, and overall survival, than education in patients with AD. METHODS: The study included 478 probable AD patients (322 women and 156 men, mean age 74.5 years) followed in a large AD referral center for a mean of 3.2 years. Eligible participants had a baseline estimate of premorbid IQ using the American version of the Nelson Adult Reading Test (AMNART) and at least one follow-up visit with complete neuropsychological assessment. We used random effects linear regression analysis, and Cox proportional hazards analysis to determine whether or not education and/or premorbid IQ were independently associated with cognitive decline, global progression of AD, and survival. RESULTS: When the baseline AMNART score was included in regression models along with education and other demographic variables, AMNART score, but not education, was associated with a higher baseline score and slower rate of decline in MMSE and ADAS-Cog scores, and the Clinical Dementia Rating sum of boxes score. Neither higher premorbid IQ nor higher education was associated with longer survival. CONCLUSIONS: We conclude that a baseline AMNART score is a better predictor of cognitive change in AD than education, but neither variable is associated with survival after diagnosis.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".