Correlation between Vegetable and Fruit Intake and Cognitive Function in Older Adults: A Cross-Sectional Study in Chongqing, China
Bibliographic record
Abstract
OBJECTIVE: To explore the correlation between different types of vegetable and fruit intake and cognitive function among the older adults in Chongqing, China, and to provide a scientific basis for developing efficient lifestyle interventions for the prevention of Mild Cognitive Impairment (MCI). METHOD: Approximately 728 older adults in urban and rural areas of Chongqing were surveyed using face-to-face questionnaires. Cognitive function was assessed with the Montreal Cognitive Assessment-Basic (MoCA-B) scale, and the vegetable and fruit intake groups were investigated with the Simple Food Frequency Counting Survey Scale. Binary logistic regression was used to explore the effect of the vegetable and fruit intake group on cognitive function. Subgroup analysis was used to demonstrate the robustness of the results. RESULT: < 0.05). Only lower intake (Q2) of total vegetables, medium intake (Q2, Q3) of solanaceous vegetables, and medium-high intake (Q2, Q4) of fungi and algae was protective against MCI, whereas the leafy vegetables showed no relation to MCI. Apart from this, participants who were older, female, unmarried, non-smoking, and engaged in physical labor, and who had an average monthly income of less than 3000 RMB were more likely to suffer from cognitive impairment. CONCLUSION: This suggested that the fruit-intake groups and some vegetable-intake groups showed a protective effect on cognitive function, and might behave differently depending on their different intake and demographic characteristics. A sensible, healthy diet can help prevent MCI.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".