Do Education and Premorbid Intelligence Predict Cognitive Decline Over 1 Year in Rural Patients with Dementia?
Bibliographic record
Abstract
INTRODUCTION: Education and premorbid intelligence have been used to predict disease trajectory in dementia. However, there is conflicting evidence regarding their independent predictive ability. This study aims to investigate whether education and premorbid intelligence predict cognitive and functional decline in rural dementia patients over 1 year. METHODS: Data from 614 rural patients was analyzed for the association between (a) years of education and (b) premorbid intelligence [Premorbid Verbal IQ] and cognitive function [MMSE, CDR-SB, NPI, FAQ] through linear regression analysis as an overall group sample, and stratified into Subjective Cognitive Impairment, Mild Cognitive Impairment, Alzheimer Disease, and Non-Alzheimer Disease dementia groups. Premorbid verbal IQ was estimated using the Wechsler Test of Adult Reading & Wide Range Achievement Test 4th Edition, after norming each was scored on a scale of 100. RESULTS: Higher Premorbid Verbal IQ score predicted a smaller decline in cognition in the overall group sample and reduced caregiver dependence in the non-AD dementia group 1-year post-diagnosis. Education was not a statistically significant predictive factor of cognition 1-year post-diagnosis. CONCLUSION: Higher premorbid intelligence may be a better 1-year prognostic indicator of cognition and function than education level in rural populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".