Nutritional Therapies and Their Influence on the Intestinal Microbiome in Pediatric Inflammatory Bowel Disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic, autoimmune disorder of the gastrointestinal tract with numerous genetic and environmental risk factors. Patients with Crohn's disease (CD) or ulcerative colitis (UC) often demonstrate marked disruptions of their gut microbiome. The intestinal microbiota is strongly influenced by diet. The association between the increasing incidence of IBD worldwide and increased consumption of a westernized diet suggests host nutrition may influence the progression or treatment of IBD via the microbiome. Several nutritional therapies have been studied for the treatment of CD and UC. While their mechanisms of action are only partially understood, existing studies do suggest that diet-driven changes in microbial composition and function underlie the diverse mechanisms of nutritional therapy. Despite existing therapies for IBD focusing heavily on immune suppression, nutrition is an important treatment option due to its superior safety profile, potentially low cost, and benefits for growth and development. These benefits are increasingly important to patients. In this review, we will describe the clinical efficacy of the different nutritional therapies that have been described for the treatment of CD and UC. We will also describe the effects of each nutritional therapy on the gut microbiome and summarize the strength of the literature with recommendations for the practicing clinician.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".