One third of dementia cases can be prevented within the next 25 years by tackling risk factors. The case “for” and “against”
Bibliographic record
Abstract
BACKGROUND: Recently, it has been suggested that up to a third of the dementia cases might be preventable. While prevention is always better than cure, this is particularly important in the field of dementia, as current interventions are not able to modify the disease. This article revises the evidence "for" and "against" dementia primary prevention. DISCUSSION: Evidence "for" is sustained by the Lancet Commission on Dementia Prevention, Intervention and Care that noted a reduction of age-related incidence of dementia in high-income countries. Based on results from large cohort studies and using population attributable risk, the commission concluded that up to 35% of dementia cases could be prevented by modifying nine risk factors: low education, midlife hearing loss, obesity, hypertension, late-life depression, smoking, physical inactivity, diabetes, and social isolation. In this life course conceptual framework, modifications of risk factors can influence dementia decades before clinical disease onset. However, evidence "against" is supported by large randomized controlled trials (RCT, > 250 participants per arm, minimum of 6 months follow-up), primarily set to prevent dementia using lifestyle interventions that have shown modest or negative results. The 2017 National Academy of Medicine report concluded that the current evidence is limited and there are no specific interventions to warrant a public health recommendation for dementia prevention. Multiple pathological pathways are involved in the development of dementia which are theoretically treatable by managing midlife hearing loss and hypertension, and with physical exercise and education, as suggested by robust observational studies. However, evidence from large clinical trials is not conclusive to support that a third of dementia cases might be prevented. Current initiatives testing the effect of lifestyle interventions in larger clinical trials may help to settle this debate.
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".