Challenges moving forward with economic evaluations of exercise intervention strategies aimed at combating cognitive impairment and dementia
Bibliographic record
Abstract
Cognitive decline among adults aged 65 years and older is a substantial public health problem in terms of incidence, health burden to the individual and care givers, as well as healthcare-related costs.1 With the world's ageing population increasing, the number of older adults with dementia is estimated to rise from 26.6 million in 2007 to 106.2 million in 2050.2 The economic burden of cognitive impairment and dementia cannot be ignored. In 2000, dementia was the third most costly health condition to care for in the USA, with annual costs estimated at $100 billion (in 1997 US prices). Another study calculated that the mean annual cost for dementia care is €28 000 per patient.3 The direct costs in the UK of Alzheimer's disease were estimated at £23 billion annually.4 Clearly, the cost of care for dementia is extremely high, and any strategies that delay the onset and/or slow the progression of cognitive decline and dementia can have enormous societal return in terms of costs and consequences. To date, effective pharmacotherapy for cognitive decline remains a challenge.1 Rather, recent evidence emphasises the importance of behavioural strategies such as physical activity to promote cognitive function.5,–,10 Specifically, results from randomised controlled trials suggest that exercise has benefits for cognitive function among cognitively normal older adults5,–,7 and among older adults with mild cognitive impairment.8 9 However, more research is needed to ascertain the direct effect of exercise on cognition among those with dementia, such as Alzheimer's disease and vascular dementia. Nevertheless, as previously highlighted by Erickson and Kramer,10 physical activity provides clear benefits for cognition among seniors. These neuroscientists contend that ‘physical activity is an inexpensive treatment that could have substantial preventive and restorative properties for cognitive and …
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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 | 0.192 | 0.465 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 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".