Broadening dementia risk models: building on the 2024 Lancet Commission report for a more inclusive global framework
Bibliographic record
Abstract
The 2024 Lancet Commission Report on dementia prevention has identified 14 modifiable risk factors that account for approximately 45% of global dementia cases. We used a global multidimensional approach that integrates gender equity considerations, poverty, wealth shocks, income inequality and HIV infection rates to identify additional risk factors beyond those reported in 2024 report. This methodological framework aims to enhance equitable prevention strategies to mitigate the global burden of dementia. We demonstrate that adding four additional risk factors: poverty, wealth shocks, income inequality, and HIV, while also considering the influences of sex and gender will improve the global applicability of the 2024 report. This is important because, despite dementia primarily affecting women, 57% of the risk factors identified in the 2024 report are more prevalent in men. Our analysis suggests that incorporating these four additional factors could potentially increase the proportion of preventable dementia cases to about 65%. This approach would also reshape the understanding of dementia risk, indicating that around 56% of modifiable risks disproportionately impact women. Expanding risk models in this manner is crucial for developing equitable and effective global dementia prevention strategies, particularly in underrepresented regions. We present these considerations as enhancements to the Commission's significant work.
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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 | 0.097 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".