Dementia prevention: Raising awareness about dementia and risk reduction
Bibliographic record
Abstract
Dementia prevention: Raising awareness about dementia and risk reduction We hear from Dr Anthony J. Levinson, who is part of an academic group developing evidence-based online resources to complement dementia prevention strategies and support care partners. The prevalence of dementia is increasing as our population ages. From a public health standpoint, we need to continue to try to prevent or delay conditions that lead to dementia while also striving to better support people living with dementia and their family/friend care partners. While age and other factors like genetics are important non-modifiable risk factors, there is increasing evidence that several modifiable risk factors account for up to 40% of dementias. While some factors – such as physical activity – may be familiar to some, other factors, such as hearing loss, blood pressure, or social activity, may be much less well-known to the public as risk factors for dementia. For individuals newly diagnosed with dementia or family/friends trying to support and care for their loved ones, they may have very little knowledge about the condition and what to expect. This is where access to easy-to-understand educational content about dementia can be beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".