The effect of fruit and vegetable consumption on the decreased risk of mild cognitive impairment in patients referring to health examination centers ‒ A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Introduction: nutritional factors play an essential role in the occurrence of cognitive impairment. The present study aimed to investigate the effect of fruit and vegetable consumption on the decreased risk of mild cognitive impairment in patients referring to health examination centers. Methods: a comprehensive search was conducted across five electronic bibliographic databases. Two groups of keywords were selected for search in the databases. Two independent researchers screened and selected the studies by examining the titles and abstracts. Two evaluators gathered essential details from the chosen studies. To assess the quality of the studies, appraisal instruments from the Joanna Briggs Institute (JBI) were utilized. Moreover, a meta-analysis was performed focusing on the values of the odds ratio. Results: 38 studies were entered into the study; 36 studies (94.7 percent) revealed that fruit and vegetable consumption can affect the occurrence of mild cognitive impairment. The values of the odds ratio related to the effect of vegetable consumption and fruit intake on the decreased risk of mild cognitive impairment were between 0.20 to 0.82 and 0.14 to 0.92, respectively. The results showed that vegetable consumption [odds ratio: 0.60 (95 % CI: 0.40 to 0.79)] and fruit intake [odds ratio: (95 % CI: 0.46 to 0.86)] can decrease the risk of MCI. Conclusions: the results revealed that fruit and vegetable consumption can significantly decrease the risk of mild cognitive impairment. Therefore, it is suggested that preventive plans for fruit and vegetable consumption should be implemented, especially in low- and middle-income countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".