Are cognitively intact seniors with subjective memory loss more likely to develop dementia?
Bibliographic record
Abstract
BACKGROUND: Subjective memory loss (SML) is common in elderly persons. It is not clear if SML predicts the development of dementia. OBJECTIVES: (1) to determine if SML in those with normal cognition predicts dementia or cognitive impairment without dementia (CIND); (2) to determine if an association is independent of the effect of age, gender and depressive symptoms. METHODS: Secondary analysis of the Manitoba Study of Health and Aging (MSHA), a population-based prospective study. Data were collected in 1991, and follow-up was done 5 years later. Community-dwelling seniors sampled randomly from a population-based registry in the Canadian province of Manitoba, stratified on age and region. Only those scoring in the normal range of the Modified mini-mental state examination (3MS) were included. Predictor variables were self-reported memory loss, 3MS, Center for epidemiological studies-depression scale (CES-D), age, gender, and education. Outcomes were mortality and cognitive impairment five years later. RESULTS: In bivariate analyses, SML was associated with both death and dementia. In multivariate models, SML did not predict mortality. After adjusting for age, gender, and depressive symptoms, SML predicted dementia. However, after adjusting for baseline 3MS score, SML did not predict dementia. CONCLUSIONS: Memory complaints predict the development of dementia over five years, and clinicians should monitor these persons closely. However, the proportion of persons developing dementia was small, and SML alone is unlikely to be a useful clinical predictor of dementia.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".