Dupilumab in Elderly Patients With Severe Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) in the elderly has been poorly investigated, although its incidence is gradually increasing mainly in industrialized countries. Age-specific factors in older patients must be considered when selecting treatment options. OBJECTIVES: To evaluate the efficacy and tolerability of dupilumab in treating elderly patients with severe AD. METHODS: This was a retrospective, multicenter study involving 26 elderly patients (age, ≥65 years) with severe AD who were treated with dupilumab for at least 16 weeks. Absolute and percentage frequencies were used to evaluate qualitative variables and mean and SD for quantitative ones. For Eczema Area and Severity Index (EASI), Pruritus Numeric Rating Scale (NRS), and Dermatology Life Quality Index (DLQI), the median was also calculated. Wilcoxon test was used to evaluate the variations in EASI, Pruritus NRS, and DLQI observed between the 2 examinations. RESULTS: After 4 months of therapy, the majority of patients showed a significant improvement in EASI (64.4%), Pruritus NRS (58.2%), and DLQI (44.9%). Only 11% of patients reported mild or moderate conjunctivitis. CONCLUSIONS: To the best of our knowledge, this is the first study concerning the use of dupilumab in the elderly with severe AD. Our data show the effectiveness of dupilumab in this particular population with a lower percentage of conjunctivitis than observed in studies on adults and also excellent control of itching. Only larger, controlled case studies will be able to clarify whether the dosage or frequency of administration of dupilumab in these patients should be different from the protocol used for adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".