Impact of Simultaneous Itch Relief and Substantial Skin Clearance With Lebrikizumab in Patients With Atopic Dermatitis: Post-hoc Analysis From ADvocate1/2
Bibliographic record
Abstract
■ AD imposes substantial disease burden on patients and impairs HRQoL due to chronic itching and skin inflammation.1■ Lebrikizumab is a monoclonal antibody that inhibits interleukin-13 signaling 2 and has been approved for the treatment of moderate-to-severe AD. 3■ In the phase 3 ADvocate1 (NCT04146363) and ADvocate2 (NCT04178967) trials, 4 patients more frequently achieved ≥75% improvement from baseline in the Eczema Area and Severity Index (EASI 75) plus ≥4-point improvement from baseline in the Pruritus Numerical Rating Scale score (Pruritus NRS improvement ≥4) with lebrikizumab than with placebo (31.9% vs 5.2%) at week 16. 5 ■ Real-world data suggest that achieving optimal treatment targets for skin clearance and itch is associated with better HRQoL and patient-reported outcomes in patients with AD. 6,7 ■ This post hoc analysis evaluated the impact of achieving simultaneous itch relief and skin clearance at week 16 on HRQoL and disease burden in patients treated with lebrikizumab, using pooled data from ADvocate1 and ADvocate2.■ The results showed that achieving both itch relief and skin clearance was associated with greater odds of attaining a clinically meaningful outcome compared to partial improvements in itch or skin alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".