Nutrition practice and research in the age of nutritional neuroscience
Bibliographic record
Abstract
Abstract Objective To explore current issues regarding the inclusion of nutritionists in Nutritional Neuroscience, addressing key concepts, main areas of research, and their potential, in addition to knowledge gaps requiring further attention. Methods This theoretical and reflective article discusses major research topics in Nutritional Neuroscience, including eating behavior and its influence on human health, the relationship between nutrition and nutritional status and cognitive function (memory and mood disorders), the role of nutrition in neurodevelopmental disorders, its implications for the treatment of neurological diseases and epilepsy. This discussion is supported by scientific literature and clinical guidelines and protocols developed by specialized agencies in food, nutrition, and medical care. Results Nutritional Neuroscience examines the interplay between brain function and food intake, aiming to broaden the understanding of how dietary habits, nutrient consumption, and nutritional status influence brain function, as well as their implications in normal homeostatic processes and their impact on brain health, neurobiological mechanisms, and pathological conditions. In this article, we address the aspects of eating behavior and the role of nutrition in psychiatric and neurodevelopmental disorders, memory, and neurological diseases, which are areas considered the most prominent and promising within the field. Conclusion Nutritional Neuroscience represents a promising field for nutritionists in both research and professional practice. Strengthening the educational foundation of nutritionist training by integrating the best available evidence is essential to support effective and evidence-based practice in this area.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".