Behavioral correlates of subjective cognitive decline in the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Abstract Subjective cognitive decline (SCD) is a self-reported decline in cognition among otherwise cognitively healthy older adults. It is believed that SCD may be a precursor to Alzheimer’s Disease (AD). Analyzing data from the Canadian Longitudinal Study on Aging (CLSA), a large national sample of participants aged 45-85 at baseline, we sought to identify prospective relationships between health-related behaviors and SCD. Exposures were measured at baseline and SCD was measured three years later, with the question: “Do you feel like your memory is becoming worse?”. A multivariable logistic regression model was used to estimate odds of SCD (analytic sample: n=35,680). Alcohol consumption was associated with increased odds of SCD, with regular drinkers (OR=1.13, 95% CI: 1.04, 1.22) and frequent drinkers (OR=1.17, 95% CI: 1.08, 1.27) more likely to report SCD than never drinkers. Compared to participants who never smoked, former smokers had increased odds of SCD (OR=1.13, 95% CI: 1.08, 1.18), whereas current smokers had reduced odds of SCD (OR=0.90, 95% CI: 0.83, 0.98). Participants who consumed five or more servings of fruits/ vegetables had reduced odds of SCD (OR=0.95, 95% CI: 0.91, 0.99), when compared to those who consumed <5 servings. Lastly, we did not observe any associations between walking and SCD. This study identifies relationships between various health-related behaviors and SCD in a large population-based sample of older Canadians. Identification of modifiable risk factors may help with early prevention and intervention of SCD.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".