Non-immunoglobulin E-mediated food allergy
Bibliographic record
Abstract
Non-immunoglobulin E (IgE)-mediated food allergies are characterized by delayed gastrointestinal (GI) manifestations that occur after exposure to an inciting food protein; they include food protein-induced allergic proctocolitis (FPIAP), food protein-induced enteropathy (FPE), and food protein-induced enterocolitis syndrome (FPIES). Although the exact mechanisms underlying these disorders are not well understood, non-IgE-mediated food allergies likely represent a spectrum of disease with shared pathophysiological processes. Typically, these non-IgE-mediated food allergies begin in infancy or early childhood, although FPIES can present across the lifespan, with increasing reports in adults in recent years. Diagnosing non-IgE-mediated food allergies can be challenging due to the lack of noninvasive confirmatory tests or biomarkers for most of these disorders and the non-specific nature of GI symptoms. Thus, the diagnosis is usually made clinically, and relies on a constellation of typical symptoms that improve upon removal of the culprit food. The primary approach to management of FPIAP, FPE and FPIES is avoidance of the triggering food, and a multidisciplinary management approach that includes allergy/immunology may be required to avoid unnecessary food restriction and guide food reintroduction. This review outlines the clinical manifestations, epidemiology, pathophysiology, diagnosis, management, and prognosis of these non-IgE-mediated food allergies.
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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) | 0.005 | 0.003 |
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".