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Record W3176421675 · doi:10.3390/nu13062109

Nutritional Aspects of Pediatric Gastrointestinal Diseases

2021· review· en· W3176421675 on OpenAlexaff
Teresa Di Chio, Christiane Sokollik, Diego Peroni, Lara Hart, Giacomo D. Simonetti, Franziska Righini‐Grunder, Osvaldo Borrelli

Bibliographic record

VenueNutrients · 2021
Typereview
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIntensive care medicineDiseasePopularityMedical nutrition therapyMEDLINEPsychologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.052
GPT teacher head0.344
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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