Summary of the Best Evidence for Dietary Nutrients Management to Promote Brain Health in Community-dwelling Residents
Bibliographic record
Abstract
Background Maintaining brain health is an advanced goal of healthy ageing. Eating a diet with appropriate nutrients has been identified as a promising approach to reducing the risk of dementia, but community-dwelling residents have inadequate or no health guidance from medical workers since there is no detailed and comprehensive clinical dietary nutrients management program for brain health promotion for community-dwelling people . Objective To search, evaluate and summarize the evidence on dietary nutrients management for brain health promotion in community-dwelling people, providing an evidence-based basis for clinical implementation of such management for this group of population. Methods In March 2022, we searched UpToDate, BMJ Best Practice, JBI Model of Evidence-based Healthcare, National Institute on Aging, Registered Nurses' Association of Ontario, the Cochrane Library, PubMed, CNKI, Medlive and other databases to collect literature (involving clinical decisions, recommended practices, guidelines, evidence summaries, expert consensuses and systematic reviews) regarding dietary nutrients management for brain health promotion in community-dwelling people. The retrieval period was from January 1, 2017 to March 29, 2022. Two researchers systematically trained in evidence-based medicine independently screened literature, extracted data, and evaluated the quality of the included literature. Then evidence was extracted from the included literature, and its quality was graded, then the best evidence was summarized. Results A total of 28 studies with an overall high methodological quality were included, including three clinical guidelines, five expert consensuses, one clinical decision and 19 systematic reviews. Finally, 23 pieces of best evidence were summarized, involving seven aspects of intervention timing, dietary nutrients assessment and screening, dietary patterns and components, specific nutrients, coffee intake, body weight management, health education and guidance. Conclusion Eating a diet with appropriate nutrients can promote brain health of residents. Community medical workers should develop an individualized dietary nutrients management program for brain health for them in accordance with the practical clinical situation, residents' current dietary nutrients status and preferences, and insights from the best evidence.
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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 | 0.020 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".