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Record W4391558728 · doi:10.1089/derm.2023.0373

Nonsteroidal Approaches for Atopic Dermatitis®: A Clinical Update

2024· review· en· W4391558728 on OpenAlexvenueno aff
Peter Ch’en, Peter Lio

Bibliographic record

VenueDermatitis · 2024
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNonsteroidalAtopic dermatitisAdverse effectExacerbationDermatologyIntensive care medicineJanus kinaseHypopigmentationPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Topical corticosteroids (TCSs) are the most widely used treatment for atopic Dermatitis® (AD), but they can have adverse effects such as skin atrophy, telangiectasias, and hypopigmentation, especially with prolonged use of higher potency steroids. Many patients also have a fear of using TCSs, known as "corticophobia." With the development of biologics and Janus kinase inhibitors, a nonsteroidal approach to the treatment of AD may be possible and may be preferred by certain patients. Given what is known about these nonsteroidal therapies, we propose a structured treatment ladder and action plan that can guide clinicians and patients on the use of these therapies for the treatment of AD. The ladder divides nonsteroidal medication classes into treatments for exacerbation versus maintenance therapies in an escalating order of increasing potential for adverse effects, both real and perceived. This treatment algorithm proposal paves the way for a potential nonsteroidal approach to managing AD.

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)0.001
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.120
GPT teacher head0.389
Teacher spread0.269 · 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
Published2024
Admission routes1
Has abstractyes

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