Why so negative about preventing cognitive decline and dementia? The jury has already come to the verdict for physical activity and smoking cessation
Bibliographic record
Abstract
The world is ageing rapidly, and accompanying this demographic transition will be a significant increase in the number of people with dementia, a condition that will affect the developing world more greatly than the developed world, in both absolute numbers and proportional increase.1 The human and financial costs of this condition have, not surprisingly, been of concern to older people, their families and policy makers around the world as they grapple with what will eventually be a major cause of life years lost to disability. The search is on for safe, effective and hopefully, affordable ways to prevent this common and devastating condition of older people. Against this background, it was timely for the US Department of Health and Human Services National Institutes of Health (NIH) to host a consensus conference on ‘Preventing Alzheimer’s Disease and Cognitive Decline,'2 supported by an Evidence Based Review.3 Many of the other scourges of old age have already demonstrated a reduction in age specific incidence, and thus there is hope that similar outcomes may be achieved for dementia. For example, there has been a 25% reduction in age-adjusted stroke death rates in the USA,4 an observation confirmed in Australia where further data indicate that this is more likely to be due to a decreased incidence of stroke rather than an improvement in survival following a stroke.5 Similarly, after an earlier increase, age-adjusted female hip fracture incidence decreased between 1995 and 2005 by about 25%.6 Therefore, prevention of these common problems of older people may be achievable and a goal for which it is worth striving. The degree of difficulty in ascertaining the population prevalence and incidence of dementia, and also the fact that many of the higher-quality studies were completed over two decades ago, makes it unlikely that …
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.029 | 0.049 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".