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Record W4387910407 · doi:10.2147/phmt.s404779

Diagnostic and Management Strategies of Food Protein-Induced Enterocolitis Syndrome: Current Perspectives

2023· review· en· W4387910407 on OpenAlexaff
Angela Mulé, Catherine Prattico, Adnan Ali, Pasquale Mulé, Moshe Ben‐Shoshan

Bibliographic record

VenuePediatric Health Medicine and Therapeutics · 2023
Typereview
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsMontreal Children's HospitalMcGill University Health Centre
Fundersnot available
KeywordsOral food challengeMedicineEnterocolitisFood allergyNatural historyIntensive care medicinePopulationAllergyImmunologyPediatricsPathologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Food protein-induced enterocolitis syndrome (FPIES) is a form of non-IgE mediated food allergy that presents with delayed gastrointestinal symptoms after ingestion of the trigger food. The data regarding FPIES are sparse, despite being recognized as a distinct clinical entity. This narrative review presents the characteristics of this disorder in the pediatric population, as well-standard diagnostic and management protocols. FPIES can be classified into acute and chronic subtypes, and some cases may develop into an IgE-mediated allergy. Given that skin prick tests and specific IgE levels are negative in the majority of cases, diagnosis relies on clinical history and oral food challenges. Management involves elimination diets, assessment of tolerance through oral food challenges, and rehydration in the event of a reaction. Future research should focus on improving diagnostic methods, illustrating underlying pathogenesis and biomarkers, and assessing long-term natural history. Increased knowledge and awareness for FPIES are required.

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)0.001
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 score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.172
GPT teacher head0.447
Teacher spread0.275 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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