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Record W4399553284 · doi:10.1016/j.jaad.2024.05.081

Effectiveness of abrocitinib for the treatment of moderate-to-severe atopic dermatitis in patients switched from dupilumab and/or tralokinumab: A real-world retrospective study

2024· article· en· W4399553284 on OpenAlexaff
Samantha Keow, Mohannad Abu‐Hilal

Bibliographic record

VenueJournal of the American Academy of Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDupilumabMedicineAtopic dermatitisDermatologyRetrospective cohort studyInternal medicine

Abstract

fetched live from OpenAlex

To the Editor: Dupilumab and tralokinumab are biologic therapies approved for the treatment of moderate-to-severe atopic dermatitis. Clinical trials have shown that 44% to 52% and 25% to 33.2% of patients receiving dupilumab and tralokinumab, respectively, achieved a ≥75% improvement in Eczema Area and Severity Index (EASI-75) at week 16.1,2 Despite their efficacy, many patients in real-world settings either do not respond adequately to these treatments or encounter adverse events (AEs). Abrocitinib, a Janus kinase 1–selective inhibitor, has demonstrated superior efficacy compared with both dupilumab and tralokinumab.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.325
Teacher spread0.309 · 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 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

Citations7
Published2024
Admission routes1
Has abstractyes

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