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Record W4200316635 · doi:10.1097/der.0000000000000801

Characterization of Residual Facial Dermatitis during Dupilumab Therapy: A Retrospective Chart Review to Delineate the Potential Role of Expanded Series Patch Testing

2021· article· en· W4200316635 on OpenAlexvenueno aff

Bibliographic record

VenueDermatitis · 2021
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsDupilumabPatch testingConcomitantPatch testAtopic dermatitisResidual

Abstract

fetched live from OpenAlex

OBJECTIVE: We sought to determine the incidence of RFDD in patients receiving dupilumab and the rate of resolution of RFDD after expanded series patch testing (ESPT) and allergen avoidance. METHODS: This is a retrospective chart review of 80 patients with atopic dermatitis who were evaluated for RFDD after treatment with dupilumab. Expanded series patch testing findings and response to allergen avoidance were assessed in the subset of patients with RFDD who subsequently underwent ESPT while continuing to receive dupilumab. RESULTS: Forty-nine patients (61.3%) experienced facial dermatitis before initiating dupilumab. Thirty-five patients (43.8%) experienced RFDD after starting dupilumab. Of the 14 patients with RFDD who received ESPT, 92.9% had 1 or more relevant positive patch test results, with 50% of such patients being mostly to completely clear of facial dermatitis after allergen avoidance. Importantly, 50.6% of the positive reactions to allergens were not included on the North American Contact Dermatitis Group Core 80. CONCLUSIONS: Many patients with RFDD benefit from patch testing and subsequent allergen avoidance. Expanded series patch testing should be offered to patients who experience RFDD after beginning dupilumab therapy to ensure that such patients have eliminated any exogenous component of their dermatitis, such as concomitant allergic contact dermatitis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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