The Beneficial Effect of Physical Exercise on Cognitive Function in a Non-dementia Aging Chinese Population
Bibliographic record
Abstract
Numerous observational studies have shown that physical exercise promotes cognition in the elderly, however, the results from randomized clinical trials (RCTs) are ambiguous. In addition, potential benefits of exercise in an elderly Chinese population have not been comprehensively addressed. In this study, an investigation was launched which focused on the relationship between physical exercise and cognitive function, blood lipid profiles and brain anatomy in a non-dementia aging Chinese population. A total of 2074 non-dementia elderly subjects were included (self-selected exercise n=1372; self-selected non-exercise n=702). Amongst the subjects, 689 volunteered to receive blood lipid tests, 141 undergo brain magnetic resonance imaging (MRI), and 1399 receive a 1 year cognitive evaluation follow-up. The Beijing version of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental States Examination (MMSE) were used to assess cognitive function. A significant difference in cognitive function was observed at the baseline and during the one-year follow-up between the self-selected exercise and self-selected non-exercise groups, however, no significant differences in blood lipids and brain anatomy was evident. Physical exercise has a beneficial effect on cognition, particularly visuospatial function, and decreases the risk of dementia in a Chinese aging cohort.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".