Predicting Who Will Develop Dementia in a Cohort of Canadian Seniors
Bibliographic record
Abstract
OBJECTIVES: We examined whether easily attainable variables were useful in predicting who became demented over a five year period and determined the rates of incident dementia for different categories of mild cognitive impairment. METHODS: This was a cohort study of subjects recruited nationally in a population-based survey of Canadians 65 years and older (the Canadian Study of Health and Aging). After standardized clinical assessments, a subset of subjects (n = 1782) was categorized as not demented at time one. Identical study methods allowed a reassessment of the cognitive status of surviving subjects (n = 892) five years later. RESULTS: Three baseline variables (Modified Mini Mental State (3MS) score, subject's age, and an informant's report of the presence of memory problems) were statistically significant predictors of the development of a dementia. An equation incorporating these three variables had a sensitivity of 79% and a specificity of 56% for predicting dementia among survivors at time two. An equation substituting the MMSE for the 3MS showed similar results. The various categories of mild cognitive impairment examined showed significantly different likelihoods for the subsequent development of a dementia. Some categories with a higher dementia risk were characterized by inclusion criteria requiring neuropsychological test scores that were greater than one standard deviation (SD) below the mean of age based normative data. CONCLUSION: In the absence of extensive laboratory, radiologic or neuropsychological tests, simple variables that can be easily determined in the course of a single clinical encounter were useful in predicting subjects with a higher risk of developing dementia. Attempts to use neuropsychological results to predict the development of dementia should look for significant impairments on age-standardized tests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".