Therapeutic Modulation of Dupilumab in Patients with Severe Atopic Dermatitis®: Clinical Effectiveness in Real Life
Bibliographic record
Abstract
Abstract: Background: De-escalation strategies have become increasingly used in the treatment of atopic Dermatitis® (AD) patients with dupilumab. Dose spacing (DS) refers to dose reduction by dosage elongation strategies from 2 to 8 weeks between dupilumab injections, in patients with stable response to treatment or affected by numerous adverse events. Objectives: Investigate safety and clinical effectiveness of DS strategy in AD patients treated with dupilumab. Methods: A retrospective cohort study was conducted on AD patients aged ≥18 years treated with dupilumab undergoing DS. Pre–post analyses were conducted on this cohort, termed cohort A, between effectiveness outcomes at baseline, at 16 weeks of treatment, at the index date identified as the mean follow-up time between dupilumab initiation and DS, and at subsequent two follow-up visits: T1 and T2. Based on the index date, a cohort B of AD patients on dupilumab treatment not experiencing DS was then compared with cohort A for the same outcomes at the same time points. Results: Seventy-three out of 452 patients treated with dupilumab underwent DS. The mean time since treatment initiation was 28.6 months. Mean Eczema Area Severity Index (EASI) from the index date remained stable until the second follow-up visit (T2) 0.2–0.8 with no significant pre–post differences ( P > 0.05). Similar considerations can be made for mean number rating scale worst pruritus (NRSp), numerical rating scale disturbs of sleeping/sleeping disturb (NRSsd), mean Dermatology Life Quality Index (DLQI), and EASI Head and Neck. Attainment of relative outcomes remained stable for EASI75, 90, ≤ 7, DLQI ≤ 5, and NRSp ≤ 4. When compared with cohort B, no clinically significant differences were observed in mean reductions in all outcomes analyzed. Conclusions: DS in our study appears to be an effective and safe strategy in treating patients with severe AD after the initial therapeutic response.
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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) | 0.000 | 0.001 |
| 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) | 0.002 | 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".