The use of wide-scale mental agility testing to identify people at risk of dementia: crucial or harmful?
Bibliographic record
Abstract
The prevalence of dementia in the UK is rising rapidly and is predicted to double over the next 30 years. The NHS in England has been told to push for a rapid rise in dementia diagnosis rates, so that by 2015, two out of three cases are identified. The Prime Minister has raised the 'dementia challenge' as a priority for the NHS. While there is agreement on the need for action, debate arises over the nature of that intervention. Some, including Professor Alessi, argue that tools exist to support the diagnosis of mild cognitive impairment and they should be used because the disease is amenable to interventions. He believes that we need a shift in knowledge and attitude from thresholds to a continuum of cognitive impairment, from late to early stages and from effects to causes. The Montreal Cognitive Assessment (MoCa) should become part of the routine NHS Health Check after people reach age 40. Dr Fox argues on the other hand that widespread testing could lead to unnecessary anxiety and panic among those at risk and that funding should be focused on learning more about the early stages of dementia. While the concept of early testing is appealing, there is a large knowledge gap; instruments in use have not been tested in pre-dementia patients and have limited validity. While there is debate over the approach, we can agree that the economic and social impacts of this condition need to be addressed sooner rather than later.
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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 | 0.023 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".