Cognitive decline in high-functioning older adults: Reserve or ascertainment bias?
Bibliographic record
Abstract
The detection of mild cognitive impairment and dementia in high-functioning older adults can be difficult. It has also been observed that high-functioning persons show a lower prevalence of dementia than low-functioning persons. Three alternative explanations for this observation have been proposed in the literature: brain reserve capacity (BRC), cognitive reserve, and ascertainment bias. With data from a prospective, population-based study of incident dementia, the Canadian Study of Health and Aging (CSHA), we classified participants as being high- (HF) or low-functioning (LF) in three ways: educational and occupational attainment, and estimated premorbid IQ. We observed that fewer HF older adults were diagnosed with dementia after five years, which is in accordance with both the BRC and cognitive reserve models. Contrary to expectations, no difference on rate of memory deterioration was observed between those HF and LF persons who exhibited mild cognitive impairment at CSHA-1. However, HF persons who subsequently were diagnosed with dementia (CSHA-2) showed more rapid decline on five of the six memory measures over time than did LF persons diagnosed with dementia at CSHA-2. When performance on measures of memory functioning at CSHA-1 was examined for highly educated older adults, significantly more of those with dementia at CSHA-2 (n = 59) had scores falling within or below the average range in comparison to normative standards than those who continued to show no cognitive impairment (n = 145). Our findings suggest that the lower incidence of dementia for HF persons may be primarily the result of ascertainment bias, not underlying differences in brain or 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 | 0.135 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".