Triangulation to make decisions about what are modifiable risk factors and the new risk factors
Bibliographic record
Abstract
Abstract Background The 2020 Lancet Commission on dementia prevention, intervention and care estimated that up to 40% of dementia cases could be prevented by tackling 12 potentially modifiable risk factors, namely less education, hearing loss, hypertension, physical inactivity, diabetes, social isolation, excessive alcohol consumption, air pollution, smoking, obesity, traumatic brain injury, depression. As more evidence on risk factors emerges, the Lancet standing commission on dementia met to update evidence on established dementia risk factors and to consider the evidence for other risk factors. Method We used a lifecourse approach to understand how to reduce risk or prevent dementia, as many risks operate at different timepoints in the lifespan. We considered evidence for when in the lifecourse a risk factor was relevant to development of dementia as well as the size of the effect and strength of the evidence. Our interdisciplinary, international, multicultural group of experts adopted a triangulation framework, prioritising systematic reviews and meta‐analyses, performing new meta‐analyses where needed and debated and agreed on the best available evidence and its consistency. We considered whether there was evidence of disparities in impact of risk factors based on demographic characteristics, particularly ethnicity and socioeconomic status. Result Evidence for two new risk factors was considered strong enough to include this in our lifecourse model. We will present evidence for incorporation of these risk factors, including strength of evidence and potential mechanisms. We will also discuss risk factors for which evidence was not strong enough. Conclusion As more evidence about risk factors emerges we can increase our understanding of how dementia develops and how to potentially prevent it. Understanding the landscape of dementia prevention research is also helpful to appreciate where further evidence is needed and what form of evidence would be most helpful in advancing our understanding.
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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 | 0.423 | 0.697 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".