Non-IgE-Mediated Gastrointestinal Food Allergies in Children: An Update
Bibliographic record
Abstract
Non-immunoglobulin E-mediated gastrointestinal food allergic disorders (non-IgE-GI-FA) include food protein-induced enterocolitis syndrome (FPIES), food protein-induced enteropathy (FPE) and food protein-induced allergic proctocolitis (FPIAP), which present with symptoms of variable severity, affecting the gastrointestinal tract in response to specific dietary antigens. The diagnosis of non-IgE-GI-FA is made clinically, and relies on a constellation of typical symptoms that improve upon removal of the culprit food. When possible, food reintroduction should be attempted, with the documentation of symptoms relapse to establish a conclusive diagnosis. Management includes dietary avoidance, nutritional counselling, and supportive measures in the case of accidental exposure. The prognosis is generally favorable, with the majority of cases resolved before school age. Serial follow-up to establish whether the acquisition of tolerance has occurred is therefore essential in order to avoid unnecessary food restriction and potential consequent nutritional deficiencies. The purpose of this review is to delineate the distinctive clinical features of non-IgE-mediated food allergies presenting with gastrointestinal symptomatology, to summarize our current understanding of the pathogenesis driving these diseases, to discuss recent findings, and to address currents gaps in the knowledge, to guide future management opportunities.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".