Broad and deep data for dementia: Opportunities for care and cure, challenges and next steps
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The burden of dementia on individuals, families, communities and health care systems is rising globally as world populations age. The Toronto workshop on 14-15 September 2014 identified opportunities and challenges, as well as successful strategies, of sharing and linking the massive amounts of population-based health and health care data that are routinely collected (broad data) with detailed clinical and biological data (deep data) to create an international resource for research, planning, policy-development, and performance improvement. While the potential benefits to dementia cure and care are great, there are significant challenges related to data quality, data sharing and access to data; the protection of privacy; public engagement; and funding and incentives. Moving forward will require active involvement of governments, the research community, the private sector and the public. Next steps could include pursuing the possibility of creating a global centre of excellence to share and promote best practices; developing metrics to compare countries' performance over time, and conducting pilot studies to demonstrate the value of linking "broad and deep" data to discovering better therapies and improving health care services.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it