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Record W4381189998 · doi:10.1097/mop.0000000000001262

The role of nutrition, food allergies, and gut dysbiosis in immune-mediated inflammatory skin disease: a narrative review

2023· review· en· W4381189998 on OpenAlexaff
Adrienn N. Bourkas, Irene Lara‐Corrales

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2023
Typereview
Languageen
FieldMedicine
TopicHidradenitis Suppurativa and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoQueen's University
Fundersnot available
KeywordsDysbiosisMedicineMicrobiomeAtopic dermatitisGut floraImmune systemImmunologyAllergyDiseaseAlopecia areataPsoriasisFood allergyPathogenesisBioinformaticsBiologyPathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review focuses on the emerging roles of nutrition, food allergies, and gut dysbiosis, and their influence on pediatric skin conditions such as psoriasis, hidradenitis suppurativa, and alopecia areata. As the prevalence of these conditions increases, understanding the underlying mechanisms and potential therapeutic targets is crucial for clinical practice and research. RECENT FINDINGS: The review covers 32 recent articles that highlight the significance of the gut microbiome, nutrition, and gut dysbiosis in the pathogenesis and progression of inflammatory and immune-related pediatric skin conditions. The data suggest that food allergies and gut dysbiosis play a crucial role in disease pathogenesis. SUMMARY: This review emphasizes the need for larger-scale studies to determine the effectiveness of dietary changes in preventing or treating inflammatory and immune-related skin conditions. Clinicians must maintain a balanced approach when implementing dietary changes in children with skin diseases like atopic dermatitis to avoid potential nutritional deficiencies and growth impairments. Further research into the complex interplay between environmental and genetic factors is warranted to develop tailored therapeutic strategies for these skin conditions in children.

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)0.001
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 score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
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 head0.047
GPT teacher head0.364
Teacher spread0.318 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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