Umbrella review and Delphi study on modifiable factors for dementia risk reduction
Bibliographic record
Abstract
A 2013 systematic review and Delphi consensus study identified 12 modifiable risk and protective factors for dementia, which were subsequently merged into the "LIfestyle for BRAin health" (LIBRA) score. We systematically evaluated whether LIBRA requires revision based on new evidence. To identify modifiable risk and protective factors suitable for dementia risk reduction, we combined an umbrella review of systematic reviews and meta-analyses with a two-round Delphi consensus study. The review of 608 unique primary studies and opinions of 18 experts prioritized six modifiable factors: hearing impairment, social contact, sleep, life course inequalities, atrial fibrillation, and psychological stress. Based on expert ranking, hearing impairment, social contact, and sleep were considered the most suitable candidates for inclusion in updated dementia risk scores. As such, the current study shows that dementia risk scores need systematic updates based on emerging evidence. Future studies will validate the updated LIBRA score in different cohorts. HIGHLIGHTS: An umbrella review was combined with opinions of 18 dementia experts. Various candidate targets for dementia risk reduction were identified. Experts prioritized hearing impairment, social contact, and sleep. Re-assessment of dementia risk scores is encouraged. Future work should evaluate the predictive validity of updated risk scores.
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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 | 0.369 | 0.448 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".