The Nairobi Declaration—Reducing the burden of dementia in low‐ and middle‐income countries (LMICs): Declaration of the 2022 Symposium on Dementia and Brain Aging in LMICs
Bibliographic record
Abstract
Delegates of the International Symposium representing over 40 countries, met in Nairobi, Kenya, week of 5-9 December, 2022 to highlight advances in dementia prevention, diagnosis, care, and research, as well as explore the future needs of the global community.Dementia is a major threat to optimal brain health and remains a priority for the demographically everchanging world-wide population.It incurs substantial individual, societal and global cost.By 2030, the majority of the 78 million people with dementia will be living in LMICs.Upon consideration of these grave statistics and new diagnostic paradigms with available prevention and treatment strategies, we and the African Dementia Consortium (AfDC) with frontline and lived in experience call upon the global community including governments, policy makers, international economic forums, health and social care providers, together with private and public research funding agencies, research-focused organizations such as universities, non-governmental organizations, and technology and pharma to act as follows:
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".