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Record W4406167110 · doi:10.1089/derm.2024.0317

Oral Lipids/Fatty Acids Supplements and Eczema: What Is Known?

2025· review· en· W4406167110 on OpenAlexvenueno aff
Andrea Cespedes Zablah, Peter Lio

Bibliographic record

VenueDermatitis · 2025
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDermatologyFood scienceTraditional medicineChemistry

Abstract

fetched live from OpenAlex

The pathogenesis of atopic dermatitis (AD) points to skin barrier dysfunction as a critical piece of the puzzle. Deficiencies in fatty acids and ceramides-key elements of the skin barrier-have been linked to AD. Fatty acids can be separated into omega-3 and omega-6, which can be found in a variety of foods such as fish, nuts, seeds, and even plants. In dogs, supplementation with oral fatty acids has shown promising benefits. This review aims to explore whether humans can similarly benefit from these supplements based on current literature. The results of our search varied by compound type. For borage oil and evening primrose oil, evidence of their effectiveness is mixed, though they may offer some preventative benefits. Fish oil supplements appear to be effective in treating AD, as they reduce clinical scores and symptom severity. Oral ceramides, blackcurrant seed oil, and hempseed oil have yet to be thoroughly studied, but preliminary results are promising. Among the studies, the supplementation doses and duration of treatment varied extensively. The literature did not provide comparative analysis between the supplements, and data on the overall safety and tolerability of these supplements are limited. While some evidence is promising, the reliability of these products, as well as their optimal dosage and frequency, remains uncertain.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.340
Teacher spread0.315 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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