Navigating the complexity of applying nutrition evidence to individualised care: Summary of an Academy of Nutrition Sciences Position Paper
Bibliographic record
Abstract
Diet is key to the maintenance of health and crucial in the prevention and management of many diseases. Modified nutrient intake may become essential to prevent deficiency, optimise development and health or manage symptoms and disease progression. Adding to the complexity, disease and its treatment can also affect taste, appetite and ability to access and prepare foods. Coupled with this, individual requirements for energy, macronutrients and micronutrients are influenced by factors such as life stage (age, growth, pregnancy, etc.) and health status, which can affect the processes of consuming, digesting, absorbing, metabolising or excreting nutrients. First and foremost, dietary advice must be based on sound evidence if it is to achieve and maintain human health. Furthermore, the practice of nutrition and dietetics must integrate and apply the sciences of food, nutrition, biology, physiology, behaviour management, communication and must also recognise the context that society presents, including the plethora of often conflicting information on diet and health available via the internet and other media sources.
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 | 0.054 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".