Toward the Definition of Personalized Nutrition: A Proposal by The American Nutrition Association
Bibliographic record
Abstract
Personalized nutrition holds tremendous potential to improve human health. Despite exponential growth, the field has yet to be clearly delineated and a consensus definition of the term "personalized nutrition" (PN) has not been developed. Defining and delineating the field will foster standardization and scalability in research, data, training, products, services, and clinical practice; and assist in driving favorable policy. Building on the seminal work of pioneering thought leaders across disciplines, we propose that personalized nutrition be defined as: a field that leverages human individuality to drive nutrition strategies that prevent, manage, and treat disease and optimize health, and be delineated by three synergistic elements: PN science and data, PN professional education and training, and PN guidance and therapeutics. Herein we describe the application of PN in these areas and discuss challenges and solutions that the field faces as it evolves. This and future work will contribute to the continued refinement and growth of the field of PN.Teaching pointsPN approaches can be most effective when there is consensus regarding its definition and applications.PN can be delineated into three main areas of application: PN science and data, PN education and training, PN guidance and therapeutics.PN science and data foster understanding about the impact of genetic, phenotypic, biochemical and nutritional inputs on an individual's health.PN education and training equip a variety of healthcare professionals to apply PN strategies in many healthcare settings.PN professionals have greater ability to tailor interventions via PN guidance and therapeutics.Favorable policy allows PN to be more fully integrated into the healthcare system.
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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 | 0.058 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.037 | 0.092 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".