Pre-Dementia Stages and Incident Dementia in the NuAge Study
Bibliographic record
Abstract
BACKGROUND: Motoric cognitive risk syndrome (MCR) and mild cognitive impairment (MCI) are two pre-dementia stages with an overlap, which may influence the risk for dementia. OBJECTIVE: The study aims to examine the association of MCR, MCI, and their combination with incident dementia in Quebec community-dwelling older adults. METHODS: 1,063 older adults (i.e., ≥65) were selected from a population-based observational cohort study known as the "Nutrition as a determinant of successful aging: The Quebec longitudinal study" (NuAge). Participants were separated into four groups at the baseline assessment: those without MCR and MCI (i.e., cognitively healthy individual; CHI), those with MCR alone, those with MCI alone, and those with MCR plus MCI. Incident dementia was recorded at each annual visit during a 3-year follow-up. RESULTS: The prevalence of CHI was 87.2%, MCR 3.0%, MCI 8.8%, and MCR plus MCI 0.9%. The overall incidence of dementia was 2.4% and was significantly associated with MCR alone (Odd Ratio (OR) = 5.00 with 95% Confidence interval (CI) = [1.01;24.59] and p = 0.049), MCI alone (OR = 6.04 with 95% CI = [2.36;15.47] and p≤0.001), and the combination of MCR and MCI (OR = 25.75 with 95% CI = [5.32;124.66] and p≤0.001). CONCLUSION: Combining MCR and MCI increased the risk for incident dementia. These results also demonstrated that this combination is a better predictor of dementia than MCI or MCR alone.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".