Lipid intake in infants from birth to 3 years old: review of current guidelines and knowledge gaps
Bibliographic record
Abstract
Lipids are essential for child development. Nutritional recommendations are numerous, evolving over time and are often based on expert opinions more than evidence-based medicine. The objective of this review is to critically analyse the evolution of current nutritional recommendations, identify existing knowledge gaps and propose avenues for improvement to optimise infant nutrition and development. A narrative literature review on Pubmed, EMBASE and Cochrane databases (2001-22) was conducted with the keywords: 'alpha-linolenic acid, arachidonic acid, children, cholesterol, docosahexaenoic acid, eicosapentaenoic acid, guidelines, infant, long-chain (LC) PUFA, linoleic acid, lipids and dietary intakes, newborn, palmitic acid and toddler'. Among 861 articles identified, 133 were selected. The main current recommendations are issued by the French Agency for Food Safety (AFSSA), French Agency for Food, Environmental and Occupational Health and Safety (ANSES) and the Food and Agriculture Organization and World Health Organization (FAO-WHO). In infants from 0 to 3 years of age the main challenge is to increase lipid intake while maintaining an optimal omega 6/omega 3 ratio. Current recommendations are focused on polyunsaturated fatty acids, emphasising the intake of linoleic, eicosapentaenoic and docosahexaenoic acids without any specific recommendation for arachidonic acid before the age of 6 months. Points of interest, but without any recommendation, are the incorporation of milk fat, cholesterol, monounsaturated fatty acids, and saturated fatty acids for infants under 6 months. In conclusion, this article identifies knowledge gaps regarding the structural aspect of lipids and the integration of new categories of lipids in future recommendations to promote the quality of infant formulas.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".