NEW INTERNATIONAL RESOLVE TO PRODUCE RESEARCH EVIDENCE TO PREVENT AND CURE DEMENTIA
Bibliographic record
Abstract
D ementia affects more than 35 million people worldwide, a number that is expected to almost double every 20 years. Seventy per cent of the estimated annual world-wide cost of US$604 billion is spent on informal, social and direct medical care. These costs are expected to increase significantly if therapies to prevent dementia and improve care and treatment are not developed and implemented In addition to government representatives, the historic G8 meeting in December 2013 included an assembly of representatives from international industry (pharma, technology, investment, health-based), international leading researchers, and organizations representing people with dementia and their caregivers. The meeting, attended by over 150 invitees, involved discussion of the background documents provided by the meeting organizers. The meeting identified the following challenges: Focus on more research on care and support,, require data be shared internationally, focus on early diagnosis, develop international standards for dementia-friendly communities, support and encourage co-production (involve people with dementia and their carers) in care planning, establish international norms on Care Plans and Care Coordination, evaluate current care and service interventions, find ways to exploit technology including social media and mining data, and prepare to better quantify the economic impact of dementia including economic case (return on investment) for investment and care services The G8 meeting resulted in a declaration by all eight countries that they would identify a cure or a disease-modifying therapy for dementia by 2025 and to increase collectively and significantly the amount of funding for dementia research to reach that goal. Also, all countries agreed to promote and accelerate discovery and research and its transformation into innovative and efficient care and services.
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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 | 0.322 | 0.295 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.036 | 0.032 |
| Open science | 0.011 | 0.035 |
| Research integrity | 0.029 | 0.033 |
| Insufficient payload (model declined to judge) | 0.056 | 0.026 |
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".