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The Impact of Diet on Common Skin Disorders

2014· article· en· W2059708153 on OpenAlexaffvenue
Saida Rezaković, Mirjana Pavlic, Marta Navratil, Lidija Počanić, Kristina Žužul, Krešimir Kostović

Bibliographic record

VenueJournal of Nutritional Therapeutics · 2014
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsBurnaby Hospital
Fundersnot available
KeywordsInternal medicineMedicine

Abstract

fetched live from OpenAlex

The role of nutrition in the treatment of common dermatoses is often overlooked. Nevertheless, there is a large amount of evidence suggesting that diet may have an important role in the pathogenesis, as well as in determining the clinical course of common skin disorders; including acne, psoriasis, atopic dermatitis and allergic contact dermatitis. Consequently, diet could have significant preventive or therapeutic impact in these skin conditions. Psoriasis, atopic dermatitis and allergic contact dermatitis are chronic relapsing skin disorders characterised by remissions and flare-ups, requiring long-term maintenance therapy. Although acne occurs most commonly during adolescence, and rarely continues into adulthood, it has a large impact on patients' self-confidence and self-image. For each of these skin conditions, a variety of foods may lead to exacerbation of the disease and may have a significant role in increasing the risk of other comorbidities. The aim of this review is to present current knowledge on the relationship between high-fat and high glycemic index diet and acne and psoriasis. Additionally, possible role of nutritional supplementation in such will also be reviewed. And finally, the role of dietary restriction in patients with atopic dermatitis and low nickel diet, in those who are sensitive to nickel, will be discussed. Although future studies are necessary in order to evaluate the effect of diet in these skin disorders, identifying certain foods as a potential factor that could contribute to exacerbation of the disease or to development of further complications can provide important preventive measure.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.338
Teacher spread0.317 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2014
Admission routes2
Has abstractyes

Explore more

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