MétaCan
Menu
Back to cohort
Record W4414331086 · doi:10.1177/12034754251368840

Current Understanding of Seborrheic Dermatitis: Epidemiology, Burden of Disease, and Pathophysiology

2025· article· en· W4414331086 on OpenAlexaff
Irina Turchin, Lorne Albrecht, Sameh Hanna, Dimitrios Kyritsis, Wei Jing Loo, Charles Lynde, Vimal H. Prajapati, Kerri Purdy, Linda Rochette, Marni Wiseman, D. Wong, Geeta Yadav, Jensen Yeung, Melinda Gooderham

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsQueen's UniversityHealth Sciences CentreSunnybrook Health Science CentreCanadian Institute for Energy TrainingWomen's College HospitalDalhousie UniversityOakville-Trafalgar Memorial HospitalUniversity of ManitobaHôtel-Dieu de QuébecUniversity of CalgaryLynde Centre for DermatologyWestern UniversityUniversity of TorontoMcMaster UniversitySKiN HealthUniversity of British ColumbiaProbity Medical Research
Fundersnot available
KeywordsSeborrheic dermatitisMalasseziaSkin barrierMicrobiomeEtiologySeborrhoeic dermatitis

Abstract

fetched live from OpenAlex

Seborrheic dermatitis is a common chronic dermatosis predominantly affecting sebum-rich areas. The diagnosis of seborrheic dermatitis can be challenging due to its clinical resemblance to other dermatoses. Seborrheic dermatitis can significantly impact quality of life, particularly in those with pruritus and dyspigmentation involving the facial region. The pathogenesis of seborrheic dermatitis is complex, and while its etiology is not yet fully understood, current evidence points to a complex interplay between 3 key factors: skin microbiome dysbiosis involving Malassezia spp. overgrowth and alterations in bacterial composition, dysregulated inflammatory responses in the skin, and skin barrier dysfunction. This review provides an updated overview of seborrheic dermatitis epidemiology, burden of disease, and pathophysiology, highlighting the integral roles of microbiome, inflammation, and skin barrier dysfunction in its pathogenesis. This is the first in a series of 3 reviews, each addressing different aspects of seborrheic dermatitis, including its epidemiology, diagnosis, and treatment considerations.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.053
GPT teacher head0.335
Teacher spread0.282 · 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 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cutaneous Medicine and SurgerySame topicNail Diseases and TreatmentsFrench-language works237,207