Science-based policy: targeted nutrition for all ages and the role of bioactives
Bibliographic record
Abstract
Globally, there has been a marked increase in longevity, but it is also apparent that significant inequalities remain, especially the inequality related to insufficient 'health' to enjoy or at least survive those later years. The major causes include lack of access to proper nutrition and healthcare services, and often the basic information to make the personal decisions related to diet and healthcare options and opportunities. Proper nutrition can be the best predictor of a long healthy life expectancy and, conversely, when inadequate and/or improper a prognosticator of a sharply curtailed expectancy. There is a dichotomy in both developed and developing countries as their populations are experiencing the phenomenon of being 'over fed and under nourished', i.e., caloric/energy excess and lack of essential nutrients, leading to health deficiencies, skyrocketing global obesity rates, excess chronic diseases, and premature mortality. There is need for new and/or innovative approaches to promoting health as individuals' age, and for public health programs to be a proactive blessing and not an archaic status quo 'eat your vegetables' mandate. A framework for progress has been proposed and published by the World Health Organization in their Global Strategy and Action Plan on Ageing and Health (WHO (2017) Advancing the right to health: the vital role of law. https://apps.who.int/iris/bitstream/handle/10665/252815/9789241511384-eng.pdf?sequence=1&isAllowed=y . Accessed 07 Jun 2021; WHO (2020a) What is Health Promotion. www.who.int/healthpromotion/fact-sheet/en/ . Accessed 07 Jun 2021; WHO (2020b) NCD mortality and morbidity. www.who.int/gho/ncd/mortality_morbidity/en/ . Accessed 07 Jun 2021). Couple this WHO mandate with current academic research into the processes of ageing, and the ingredients or regimens that have shown benefit and/or promise of such benefits. Now is the time for public health policy to 'not let the perfect be the enemy of the good,' but to progressively make health-promoting nutrition recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".