Exploring the Relationship between Dermatology Life Quality Index, Eczema Area and Severity Index, and Sleep Numerical Rating Scale and Pruritus Numerical Rating Scale in Patients with Atopic Dermatitis Treated with Dupilumab
Bibliographic record
Abstract
Objective: Patients with atopic dermatitis (AD) experience decreased quality of life (QoL). Here we describe the relationship between severity and QoL-related scores in patients with moderate-to-severe AD treated with dupilumab. Patients and Methods: This was a real-life, retrospective, and observational study involving patients with AD treated with dupilumab. Treatment effectiveness was evaluated based on the changes in the eczema area and severity index (EASI), sleep quality numerical rating scale ,and pruritus numerical rating scale (PNRS), as well as the dermatology life quality index (DLQI). The relationship between each of them was analyzed. After the first data collection at baseline, patients were re-evaluated at 3 subsequent follow-ups (4, 8, and 12 months). Results: A total of 52 patients were enrolled in the study. At 4 months, the change in DLQI is more correlated with PNRSs ( r = 0.643, P < 0.001) than the other scores considered. At 8 months, however, the change in DLQIs correlates similarly both with PNRSs ( r = 0.644, P < 0.001) and with the change in EASIs ( r = 0.633, P < 0.001). At 12 months of treatments, however, the trend reverses and the correlation with EASIs becomes higher ( r = 0.735, P < 0.001) than PNRSs ( r = 0.0.659, P < 0.001). Conclusions: The results of our study show that the reduction in the impact on QoL for AD patients in the first months of therapy with dupilumab correlates more with the control of pruritus than with the disappearance of skin lesions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".