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Record W4323350589 · doi:10.1159/000529138

A Systematic Review of Food Protein-Induced Enterocolitis Syndrome

2023· review· en· W4323350589 on OpenAlexaff
Catherine Prattico, Pasquale Mulé, Moshe Ben‐Shoshan

Bibliographic record

VenueInternational Archives of Allergy and Immunology · 2023
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsEnterocolitisMedicinePediatricsOral food challengeMilk allergyFood allergyImmunologyInternal medicineAllergy

Abstract

fetched live from OpenAlex

Food protein-induced enterocolitis syndrome (FPIES) is a non-IgE-mediated gastrointestinal food-induced hypersensitivity disorder that occurs mostly in infants. Long considered a rare disease, a recent increase in physician awareness and publication of diagnosis of guidelines has resulted in an increase in recognized FPIES cases. We aimed to conduct a systematic review of FPIES studies in the past 10 years. A search was conducted on PubMed and Embase in March 2022. Our systematic review focused on 2 domains: (1) the most reported FPIES food triggers; and (2) the resolution rate and median age at resolution of patients with FPIES. We found that cow's milk was the most reported trigger globally. Patterns of the most common triggers varied by country, with fish being one of the most common triggers in the Mediterranean region. We also found that the rate and median age of resolution varied by trigger. Patients with FPIES to cow's milk acquired tolerance at a younger age (most by age 3 years), while fish-FPIES was more persistent (mean resolution by age 37 months-7 years). Overall, many studies found a resolution rate of 60% for any food.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0100.012
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.037
GPT teacher head0.330
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations33
Published2023
Admission routes1
Has abstractyes

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