Evaluating the Clinical Meaning of Dermatology Life Quality Index Scores between Different Phenotypes of Atopic Dermatitis in Patients before and after Biologic Therapy with Dupilumab
Bibliographic record
Abstract
Abstract: Background and Objective: Atopic Dermatitis (AD) is the most prevalent inflammatory skin disorder resulting in an intense impact on patients quality of life. The aim of this study is to evaluate the clinical meaning of the DLQI scores documented between different phenotypes of AD patients under biologic therapy with Dupilumab. Method: We conducted a retrospective analysis of 209 patients with AD treated with Dupilumab for 2 years. These patients were categorized into different clinical phenotypes. Severity of the disease was assessed by using the Eczema Area and Severity Index (EASI), Numerical Scale Rating (NRS) for sleep (NRS sleep), pruritus (NRS pruritus) and Dermatology Life Quality Index (DLQI) at baseline and subsequently at 4,12 and 24 months. Results: Our results show that the higher DLQI scores (mean: 18.6, range:9-30) achieved at T0 are associated with a prurigo nodularis AD pattern, while after 24 months (T3) of therapy with Dupilumab, the worst quality of life index results were reported in Flexural and Head-Neck combined clinical phenotypes. Conclusions: Quality of life is probably what matters most as an overall endpoint in AD. Assessing the clinical meaning of DLQI scores across different AD phenotypes could be a further aid when considering decision making factors in patient management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".