MétaCan
Menu
Back to cohort
Record W4281645582 · doi:10.7573/dic.2021-12-1

Dermatology: how to manage atopic dermatitis in patients with skin of colour

2022· review· en· W4281645582 on OpenAlexaff
Muskaan Sachdeva, Marissa Joseph

Bibliographic record

VenueDrugs in Context · 2022
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsHospital for Sick ChildrenWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineAtopic dermatitisDermatologyPresentation (obstetrics)Ethnic groupDiseaseEpidemiologySocioeconomic statusWhite (mutation)PathologyPopulationSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

Atopic dermatitis (AD) is a chronic inflammatory cutaneous disease prevalent in all skin types but can differ in pathogenesis and clinical presentation. It has been documented in the literature that AD is more prevalent in Asian and Black individuals than in white individuals. Genetic variations as well as cultural and socioeconomic factors have important implications for susceptibility to AD and response to treatment in skin of colour. In this narrative review, we discuss differences in the epidemiology, pathophysiology, clinical presentation and treatment of AD in skin of colour. Additionally, we highlight the need for greater inclusivity of non-white ethnic groups in clinical trials to develop targeted treatments for diverse populations. Moreover, awareness of differences in AD presentation amongst non-white individuals may encourage patients to seek medical care earlier, leading to timely management and improved outcomes.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.017
GPT teacher head0.283
Teacher spread0.266 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueDrugs in ContextSame topicDermatology and Skin DiseasesFrench-language works237,207