Addressing global diversity in dementia research with the COSMIC collaboration
Bibliographic record
Abstract
There is a need to study dementia risk factors more equitably across high-income countries (HICs) and low- and middle-income countries (LMICs). Cohort Studies of Memory in an International Consortium (COSMIC) is doing this by bringing together cohort studies of cognitive ageing from around the world to study dementia risk factors in a truly international way. COSMIC researchers have investigated a wide range of dementia risk factors across the diverse member studies and shown that some factors have different levels of association with dementia in different regions and populations. These differences include some cardiovascular and lifestyle factors having stronger associations with cognitive decline and dementia among Asian people than among White people. Conversely, more social factors were associated with reduced chances of mild cognitive impairment or dementia among Asian people than among White people. Interventions to prevent or delay dementia will require tailoring to optimise the local effect. COSMIC is currently developing methods to reliably assess dementia from limited data in under-resourced regions, producing dementia risk models appropriate for LMICs, and increasing its attention to genetics, biomarkers, and environmental factors across diverse regions and populations. Further, COSMIC is helping to train new researchers in LMICs, and COSMIC members are among the first to be boarded on Dementias Platform Australia, a secure data exchange platform facilitating cohort study data access by researchers anywhere in the world. We invite cohort studies from LMICs or under-studied populations to join COSMIC and help make dementia research as globally representative and collaborative as possible.
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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 | 0.124 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 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".