Nutrition as a component of dementia risk reduction strategies
Bibliographic record
Abstract
According to the Alzheimer Society of Canada, within the next generation, Canada will experience a more than doubling of individuals living with dementia and a potentially economically crippling 10-fold increase in costs to Canadians. Up to 50% of cases with dementia can be attributed to seven modifiable, predominantly vascular and/or lifestyle-associated, risk factors. Multi-modal dementia risk reduction strategies, targeting diet, exercise, mental stimulation, and vascular risk monitoring, are likely to be the most successful. Diet-related strategies need to focus on overall diet quality and not on individual foods or nutrients. High-quality diets that are associated with better cognitive function and lower dementia risk with aging are high in vegetables, fruits, nuts, whole grains, and fish and low in red meat, high-fat dairy products, sweets, and highly processed foods. It is the time to embed risk reduction strategies into our public health and healthcare infrastructure to proactively address the challenges posed by population aging.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".