Cognitive Function Changes in Older People. Results of Second Wave of Cognition of Older People, Education, Recreational Activities, NutritIon, Comorbidities, fUnctional Capacity Studies (COPERNICUS)
Bibliographic record
Abstract
BACKGROUND: Cognitive reserve explains why subjects with more years of education, professional achievement, or participation in recreational activities show less cognitive decline with aging. We hypothesize that levels of recreational travel, education, occupation, systemic health, physical performance, and current cognitive activity levels affect the trajectory of cognitive function in older, healthy people in Poland. MATERIALS AND METHODS: = 205) were examined and followed-up at 2 years. Participants completed physical and cognitive function assessments: including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and its two subtests Delayed Recall (DR) and Verbal Fluency (VF), and Trail Making Test Part B (TMT B). Factors associated with cognitive functioning were also examined. RESULTS: The MMSE result significantly decreased over 2 years. No significant decrease in other cognitive tests was noted. However, the trajectory of cognitive tests results varied between individual participants. Percentage of variance of change was explained by the following predictors: 21 in MMSE, 24 in MoCA, 8 in DR, 25 in VF, and 24 in TMT B. Age and the presence of varicose veins were significantly linked to negative changes in MMSE and MoCA scores, while working in a professional occupational status associated with a higher score. The subgroup with varicose veins did worse on the Delayed Recall subtest of MoCA. CONCLUSION: Cognitive reserve could be extended by proxies of reserve that are related to systemic health and travel activity. The latter is a combination of social, physical, and cognitive activity and potentially might serve as an intervention to improve cognitive function in older people. However, due to the limitations of this study, results should be interpreted with caution and needs to be replicated in the further studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".