Efficacy and Safety of Dupilumab in Treating Intrinsic and Extrinsic Atopic Dermatitis in Older Patients With and Without Atopic Comorbidities: A Retrospective Study
Bibliographic record
Abstract
Abstract: Background: Despite the widespread use of dupilumab in atopic dermatitis (AD) treatment, evidence supporting its use in older patients with intrinsic AD (IAD) versus those with extrinsic AD (EAD) and in those with and without atopic comorbidities remains limited. Objective: We aimed to assess the clinical efficacy and safety of dupilumab treatment in elderly patients with IAD versus EAD and in those with and without atopic comorbidities. Methods: We enrolled 113 older patients with severe AD who received dupilumab for 48 weeks. The inclusion criteria were age ≥60 years and Scoring Atopic Dermatitis ≥25. Patients were classified as IAD or EAD and based on the presence of atopic comorbidities. The primary outcome was a reduction in the Eczema Area and Severity Index, Numeric Rating Scale, Dermatology Life Quality Index, and Atopic Dermatitis Control Test. Secondary measures were the types and rates of adverse events. Results: Dupilumab treatment resulted in a substantial improvement in AD symptoms, with no significant difference between patients with IAD and EAD or those with and without atopic comorbidities. Conclusions: Dupilumab showed good efficacy and safety in improving AD symptoms in older patients, irrespective of IAD or EAD subtypes and the presence of atopic comorbidities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".