Comparing the Association Between Dementia Risk Scores and Cognitive Function Among Members of a Research‐based Community Centre for Dementia Risk Reduction
Bibliographic record
Abstract
BACKGROUND: Dementia risk scores may be useful in facilitating communication of risk to adults seeking to support their health and wellness, maintain their cognitive function, and reduce their risk of developing dementia. While various scoring algorithms exist, limited research has compared how these scores are differentially associated with cognition among community-dwelling adults. METHOD: The objective of this study was to examine the association between five dementia risk scores (CogDrisk, LIBRA, modified LIBRA [mLIBRA], CAIDE, and Brain Care Score [BCS]) and cognitive function among 253 adults without dementia who are members of a research-based community centre for dementia risk reduction (mean age = 69.6±8.9 [range = 50 - 95], 77% female). Cognitive function was assessed using Cogniciti's Brain Health Assessment (BHA) total score, with higher scores representing better cognition. Higher dementia risk scores indicate greater dementia risk, with the exception of the BCS. Generalized linear models were used to determine the associations between dementia risk scores and cognitive function. RESULT: Lower CogDrisk scores (r = -0.36, p < 0.001), mLIBRA scores (r = -0.37, p < 0.001), and CAIDE scores (r = -0.15, p = 0.02) were associated with higher BHA scores. LIBRA scores (r = -0.08, p = 0.21) and BCS scores (r = 0.07, p = 0.28) were not significantly associated with BHA scores. CONCLUSION: Findings suggest scoring algorithms that include non-modifiable sociodemographic information (age and sex) have a stronger association with cognitive function compared to those that only include modifiable factors (e.g., physical activity, hypertension, diabetes). As this study was cross-sectional in nature, future research should examine how baseline and change in dementia risk scores predict change in cognitive function over time in order to better guide implementation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".