Dementia: pharmacological and non-pharmacological treatments and guideline review
Bibliographic record
Abstract
Editor's note This is a valuable chapter and a highly topical one, which overlaps with Chapter 10. Here we have clear evidence for the value of acetylcholinesterase (AChE) inhibitors and, to a lesser extent, memantine, in all common forms of dementia, and there is also some evidence that these benefits are long-lasting. Because these studies are well-integrated and follow similar methodologies, the systematic reviews are highly informative and suggest that these drugs are effective across the range of severity of dementia, and although there are few differences between individual compounds, it is valuable to have the detailed results available for direct comparison. The current argument over who should qualify for treatment is a highly contentious one, with many arguing that all early diagnosed cases should be treated. However, in the UK, the official NICE guidelines argue that this is not cost-effective. While we realize that the chapter is long, we think that this is an exciting and fast-moving area of enquiry. No doubt much will flow from this rich seam of new therapeutic endeavour, and hopefully we may soon arrive at treatments that can significantly change the difficult path that these patients and their families follow. Introduction The prevalence estimates of the Canadian Study of Health and Aging (CSHA) suggested that 8% of all Canadians aged 65 and over meet the criteria for dementia. The corresponding figures for Alzheimer's disease (AD) were 5.1% overall (Canadian Study of Health and Aging Working Group, 1994).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".