Kimel Family Centre for Brain Health and Wellness: Protocol for a Naturalistic Longitudinal Personalized Multidomain Preference Dementia Risk Reduction Trial
Bibliographic record
Abstract
Abstract Background The Kimel Family Centre for Brain Health and Wellness is a research‐driven community centre testing the efficacy of a naturalistic longitudinal personalized multidomain preference dementia risk reduction intervention on dementia risk and cognition. The objective of this protocol is to validate this approach by following people for two years. Method Participants (n = 325) will be 50 years of age or older, without a diagnosis of dementia, and sufficiently fluent in English to complete the assessments and understand program instructors. Participants will receive a comprehensive dementia risk assessment, including both nonmodifiable and modifiable risk factors, from which they will receive a Personalized Dementia Risk Report and Program Strategy, indicating health conditions increasing dementia risk, and their risk level in five risk domains: physical activity, brain‐healthy eating, cognitive engagement, social connections, and mental wellbeing. Participants will select programs to meet their Personalized Program Strategy. We will examine the effects of this program on cognition (MoCA and Cogniciti’s Brain Health Assessment) and risk in the five domains, as a function of adherence (attendance), compared to those of 300 individuals who will complete the Canadian Consortium on Neurodegeneration’s CAN‐THUMBS Up online, educational program on modifiable dementia risk factors, called Brain Health PRO. Result We expect better maintenance of cognition and reduction in the five dementia risk domains in Kimel Family Center participants, compared to Brain Health PRO participants. Conclusion There is an urgent need to develop scalable multidomain programs for dementia prevention. This innovative approach overcomes a number of limitations present in prior multidomain dementia prevention trials. Once validated, the approach will be scaled to other community centres, to benefit a greater number of geographically dispersed individuals.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".