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Record W3201393437 · doi:10.1007/s13555-021-00586-w

Practical Management of Patients with Atopic Dermatitis on Dupilumab

2021· article· en· W3201393437 on OpenAlexaff
Kim Papp, Chih-ho Hong, M. Perla Lansang, Irina Turchin, David N. Adam, Jennifer Beecker, Robert Bissonnette, Melinda Gooderham, Carolyn Jack, Marissa Joseph, Charles Lynde, Neil H. Shear

Bibliographic record

VenueDermatology and Therapy · 2021
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsLynde Centre for DermatologyWomen's College HospitalMcGill University Health CentreUniversity of OttawaMcGill UniversityOttawa HospitalHealth Sciences CentreUniversity of TorontoSunnybrook Health Science CentreSKiN HealthHospital for Sick ChildrenInnovaderm (Canada)Probity Medical Research
FundersSanofi GenzymeSanofi
KeywordsDupilumabAtopic dermatitisMedicineStatement (logic)Adverse effectDermatologyMEDLINEFamily medicineIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Dupilumab is approved to treat moderate-to-severe atopic dermatitis (AD) in several countries in patients as young as 6 years of age. Since its approval, practical issues related to the use of dupilumab for AD have arisen, with particular interest in transitioning from current therapies and managing medication overlap, considerations for special populations of patients with AD, and management of potential adverse events. METHODS: This article aims to review the literature addressing several practical management issues related to dupilumab use for AD and to provide a framework for clinical decision-making in these circumstances and sub-populations. Each statement was reviewed, revised and voted on by authors to provide their level of agreement and degree of uncertainty for each statement. RESULTS: An agreement level > 80% was achieved for all of the statements. CONCLUSION: The expert panel provides statements considering the practical management of patients with AD taking dupilumab to inform clinical decision-making in specific but frequently encountered clinical situations.

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 score0.013
Threshold uncertainty score0.294

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 head0.013
GPT teacher head0.280
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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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