A Descriptive, Post Hoc Analysis of Efficacy and Safety of Risankizumab in Diverse Racial and Ethnic Patient Populations With Moderate-to-Severe Psoriasis
Bibliographic record
Abstract
INTRODUCTION: Historically, patients with skin of color are underdiagnosed with psoriasis and underrepresented in clinical trials. In this study, we assess the efficacy and safety of risankizumab in patients with moderate-to-severe plaque psoriasis by race and ethnicity in the open label extension LIMMitless (NCT03047395). METHODS: Patients received continuous treatment with 150 mg risankizumab through their initial trial and the open label extension. Patients self-identified their race and ethnicity. Efficacy was assessed using Psoriasis Area Severity Index (PASI) and Dermatology Life Quality Index (DLQI). Safety is reported by events/100 patient-years. RESULTS: A total of 897 patients (race: 662 White, 196 Asian, 25 Black or African American, 14 Other; ethnicity: 98 Hispanic or Latino, 799 non-Hispanic or Latino) were included in this analysis. Compared to baseline, patients had a mean percent reduction in PASI between 94.6% (Asian) and 99.3% (Black or African American) and reported mean percent improvements in DLQI ranging from 87.1% (Asian and Black or African American) to 93.7% (Hispanic or Latino) at week 100. CONCLUSION: While the data presented here comprise a small retrospective descriptive analysis and cannot detect statistical differences, efficacy of risankizumab for the treatment of moderate-to-severe plaque psoriasis appears similar across the racial and ethnic groups studied and no new safety signals were detected.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".