Nutritional Aspects of Pediatric Gastrointestinal Diseases
Bibliographic record
Abstract
In the last decade, the role of nutritional management in pediatric gastrointestinal diseases has gained increasing popularity. Disease-specific diets have been introduced as conventional treatments by international guidelines. Patients tend to more willingly accept food-based therapies than drugs because of their relatively "harmless" nature. Apart from a diet's therapeutic role, nutritional support is crucial in maintaining growth and improving clinical outcomes in pediatric patients. Despite the absence of classical "side effects", however, it should be emphasized that any dietary modification might have negative consequences on children's growth and development. Hence, expert supervision is always advised, in order to support adequate nutritional requirements. Unfortunately, the media provide an inaccurate perception of the role of diet for gastrointestinal diseases, leading to misconceptions by patients or their caregivers that tends to overestimate the beneficial role of diets and underestimate the potential adverse effects. Moreover, not only patients, but also healthcare professionals, have a number of misconceptions about the nutritional benefits of diet modification on gastrointestinal diseases. The aim of this review is to highlight the role of diet in pediatric gastrointestinal diseases, to detect misconceptions and to give a practical guide for physicians on the basis of current scientific evidence.
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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 | 0.001 | 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 | 0.000 | 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".