Composition of healthy diets for older persons
Bibliographic record
Abstract
PURPOSE OF REVIEW: This study aims to review recent evidence (2022-2023) on the role of diet in promoting healthy aging in older adults. RECENT FINDINGS: Current evidence of diet and healthy aging is limited to epidemiological studies. A healthy diet is beneficial for individual domains of intrinsic capacity, that is, cognition, locomotion, vitality, psychological, and/or sensory functions, with sex-specific differences reported. Only a few studies used the multidimensional concept of intrinsic capacity as an outcome. This review supports that a healthy diet for older adults consists of plenty of plant-based foods, adequate protein-rich foods, and healthy fats. Next to quantity, improving the quality and variety within food groups play a role in optimizing health. Data on the associations between adherence to national food-based dietary guidelines and intrinsic capacity are scarce. SUMMARY: A healthy diet, centered on plant-based foods, adequate protein-rich foods and healthy fats, potentially promotes healthy aging. Future studies may evaluate the association between adherence to healthy dietary patterns (particularly national food-based dietary guidelines) and the multidimensional concept of intrinsic capacity, with consideration of sex-specific differences. Clinical trials are warranted to inform the causal effects of diet and outcomes related to intrinsic capacity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".