Effectiveness of Tralokinumab for Moderate-to-Severe Atopic Dermatitis Involving the Head-and-Neck Area: A Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) affecting the head and neck (H&N) area poses a clinical challenge due to the unique anatomical and physiological features of this region. Despite its well-documented impact on quality of life (QoL), evidence on the effectiveness of tralokinumab in treating AD specifically in the H&N area remains limited. Objective: To assess the clinical outcomes of tralokinumab in AD patients with H&N involvement, informing clinical decision-making and improving patient care. Methods: A multicenter, retrospective cohort study across 5 Italian tertiary referral hospitals. Patients were stratified by H&N involvement, and treatment outcomes were assessed using the Eczema Area and Severity Index (EASI), EASI H&N, Investigator Global Assessment (IGA), Numerical Rating Scale (NRS) pruritus, NRS sleep, Dermatology Life Quality Index, Patient-Oriented Eczema Measure, and Atopic Dermatitis Control Tool. Results: Among 211 patients, 145 (68.7%) had H&N involvement. A significant reduction in EASI H&N scores from baseline to weeks 12–16 (38.1%) and week 24 (54.5%) was achieved. Significant improvements in IGA, NRS pruritus, and NRS sleep, and QoL measures emerged within 12–16 weeks and persisted at 24 weeks. Compared with patients without H&N involvement, those with H&N involvement exhibited a stronger atopic background and showed no evidence of reduced drug survival. Adverse events, mainly conjunctivitis, were comparable between groups. Conclusions: Tralokinumab is effective and well-tolerated in managing moderate-to-severe AD involving the H&N area, even in biologic-experienced patients. These findings support its use as a targeted therapy addressing both clinical and psychosocial burdens of H&N AD.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".