Profiling Cognitive Reserve Among Community-Dwelling Older Adults: A Latent Class Analysis
Bibliographic record
Abstract
Abstract Cognitive reserve (CR) is a multidimensional construct shaped by education, occupation, and leisure activities, yet traditional scoring approaches obscure heterogeneity among older adults. This study applied latent class analysis (LCA) to identify distinct CR profiles and examine their clinical correlates. A cross-sectional sample of 426 community-dwelling older adults aged ≥65 years in Beijing, China, was assessed using the Cognitive Reserve Index questionnaire (CRIq). Two CR profiles emerged: Class 1, characterized by low reading and leisure engagement (51.9%), and Class 2, defined by higher educational attainment and cognitively demanding occupations (48.1%). Compared with Class 1, Class 2 participants had higher Montreal Cognitive Assessment scores (26 vs. 25, p < 0.001), fewer subjective cognitive decline symptoms (3.5 vs. 4.5, p < 0.001), and lower frailty burden (Fried phenotype score: 0 vs. 1, p = 0.006). Class 2 membership was also associated with better chewing ability (p = 0.004), higher household income (p < 0.001), and greater likelihood of being male (p = 0.007). Age was inversely associated with Class 2 membership (β=–0.060, p = 0.024). These findings reveal meaningful heterogeneity in CR within aging populations and highlight the importance of stratifying older adults by CR profiles. Identifying subgroups at risk for cognitive decline and frailty provides new opportunities for targeted interventions to promote resilience for aging.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".