MétaCan
Menu
Back to cohort
Record W3162470862 · doi:10.1097/mop.0000000000001027

Novel treatments for pediatric atopic dermatitis

2021· review· en· W3162470862 on OpenAlexaff
Jennifer B. Scott, Amy S. Paller

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2021
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsSt. Clair College
Fundersnot available
KeywordsMedicineAtopic dermatitisDupilumabClinical trialJanus kinaseImmunologyDiseasePopulationDermatologyPharmacologyInternal medicineCytokine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To describe new and emerging therapies for pediatric atopic dermatitis (AD). RECENT FINDINGS: Recent investigations have highlighted the importance of type 2 immunity and interrelationships among the skin immune system, epidermal barrier, and microbiome in the pathogenesis of AD, including in infants and children. These discoveries have translated into more targeted therapy. Crisaborole ointment, a topical phosphodiesterase 4 (PDE4) inhibitor, and dupilumab, a subcutaneously injected interleukin (IL)-4 receptor inhibitor, are now Food and Drug Administration-approved. Topical agents under investigation for use in the pediatric population include Janus kinase (JAK) inhibitors, PDE4 inhibitors, an aryl hydrocarbon receptor agonist, an antimicrobial peptide, and commensal skin bacteria. Emerging systemic agents for pediatric AD include biologics targeting IL-13, the IL-31 receptor, and the IL-5 receptor, as well as oral JAK inhibitors. SUMMARY: Increased understanding of AD pathogenesis has resulted in the development of new, more targeted therapies that show promising safety and efficacy results in Phase 2 and 3 clinical trials, although long-term safety remains to be evaluated. AD is a heterogeneous disease and having choices of therapies with different mechanisms of action will allow a broader group of children and adolescents with moderate-to-severe disease to achieve disease control.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
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 head0.128
GPT teacher head0.422
Teacher spread0.294 · 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.

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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in PediatricsSame topicDermatology and Skin DiseasesFrench-language works237,207