Short-Term Evaluation of the Real-World Efficacy and Safety of Dupilumab for the Treatment of Moderate-to-Severe Atopic Dermatitis: A Canadian Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Systemic therapy for atopic dermatitis (AD) has been challenging with limited safe and efficacious long-term treatment options. In 2017, dupilumab was approved in the United States, Europe, and Canada as the first targeted therapy for patients with moderate-to-severe AD. Despite promising efficacy and safety results in clinical trials, our understanding of dupilumab in clinical practice remains limited with few studies outside clinical trials in literature. OBJECTIVE: The aim of this study is to evaluate the efficacy and safety of dupilumab in clinical practice and discuss any differences in results between clinical trials and real-world results. METHODS: A retrospective chart review was conducted of consecutive patients receiving dupilumab treatment at two tertiary hospitals in Toronto, Canada, between December 2017 and May 2019. The primary efficacy endpoint was measured by Investigator's Global Assessment (IGA) score of 0/1 at 16 weeks and all adverse events (AEs) experienced by patients were recorded. RESULTS: Of the 93 patients included in the study, 51 (55%) reached IGA 0/1 and 38 (41%) experienced ≥1 AE. There were no severe AEs or discontinuation prior to 16 weeks due to an AE. CONCLUSIONS: These findings suggest a higher IGA-based efficacy profile with no newly identified safety concerns in patients treated with dupilumab at two tertiary hospitals in Toronto, Canada, compared to those in randomized controlled trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".