Association of dietary intake and lifestyle pattern with mild cognitive impairment in the elderly
Bibliographic record
Abstract
OBJECTIVES: Specific diets or lifestyles have an impact on cognitive function in previous studies. However, the association of the complex action of Chinese daily diets and lifestyle patterns with mild cognitive impairment (MCI) among elderly people had not been studied exactly. The aim of this study was to explore the association of dietary and lifestyle patterns with MCI among elderly people in Beijing. DESIGN: Population-based and case-control design. SETTING: The physical examination center in Xuanwu Hospital, Capital Medical University in Beijing. PARTICIPANTS: A total of 404 subjects, aged 60 years old or above, with or without MCI. MEASUREMENTS: The Montreal cognitive assessment (MoCA) was used to screen the subjects with MCI. All subjects were required to complete a questionnaire which was comprised of their demographic information, health status, lifestyles, and food frequency questionnaire (FFQ). Binary multivariable logistic regression was used to evaluate the potential association between MCI and these factors. RESULTS: With adjustment for some factors, higher daily intake of eggs (OR, 0.975, 95% CI, 0.959-0.992, P=.003) and marine products (OR, 0.96, 95% CI, 0.943-0.979, P=.000), longer time of watching TV (OR, 0.763, 95% CI, 0.628-0.928, P=.007), reading (OR, 0.540, 95% CI, 0.379-0.769, P=.001) and physical exercise (OR, 0.382, 95% CI, 0.185-0.789) had significantly decreased odds of suffering from MCI compared with the control group. CONCLUSIONS: Our findings suggested that daily higher intake of eggs and marine products, watching TV, reading and physical exercise were associated with preventing the development of MCI in this population-based samples.
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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 | 0.000 | 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 | 0.000 | 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".