Nutrition in Pediatric Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: A growing body of evidence supports the need for detailed attention to nutrition and diet in children with inflammatory bowel disease (IBD). We aimed to define the steps in instituting dietary or nutritional management in light of the current evidence and to offer a useful and practical guide to physicians and dieticians involved in the care of pediatric IBD patients. METHODS: A group of 20 experts in pediatric IBD participated in an iterative consensus process including 2 face-to-face meetings, following an open call to Nutrition Committee of the European Society of Pediatric Gastroenterology, Hepatology and Nutrition Porto, IBD Interest, and Nutrition Committee. A list of 41 predefined questions was addressed by working subgroups based on a systematic review of the literature. RESULTS: A total of 53 formal recommendations and 47 practice points were endorsed with a consensus rate of at least 80% on the following topics: nutritional assessment; macronutrients needs; trace elements, minerals, and vitamins; nutrition as a primary therapy of pediatric IBD; probiotics and prebiotics; specific dietary restrictions; and dietary compounds and the risk of IBD. CONCLUSIONS: This position paper represents a useful guide to help the clinicians in the management of nutrition issues in children with IBD.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".