An Analysis of Patient-reported Outcomes in IXORA-S: Comparing Ixekizumab and Ustekinumab over 52 Weeks in Moderate-to-severe Psoriasis
Bibliographic record
Abstract
Patient-reported outcomes are valuable for assessing new psoriasis therapies. This study investigated patient-reported outcomes in patients with moderate-to-severe plaque psoriasis treated with ixekizumab or ustekinumab, dosed according to their respective labels, for 52 weeks (IXORA-S-NCT02561806). Patient-reported outcomes investigated included patient global assessment, pruritus, skin pain, health-related quality of life, and work productivity. Ixekizumab-treated patients reported greater improvements in patient-reported outcomes sooner after treatment compared with ustekinumab-treated patients, and maintained greater improvements in patient global assessment scores (ixekizumab 0.72, ustekinumab 1.19; p < 0.001), rates of Dermatology Life Quality Index (0, 1) (ixekizumab 71.3%, ustekinumab 56.6%, p < 0.01), and 36-item Short-form Health survey physical component summary score change from baseline (ixekizumab 5.53, ustekinumab 3.28; p < 0.05) at week 52. While clinically meaningful improvements in patient-reported outcomes resulted with either treatment, ixekizumab provided more rapid improvements in patient-reported outcomes and superior outcomes for some assessments through one year of treatment, while maintaining statistically superior improvements in skin severity, as assessed by either physicians or patients.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".