Real-World Evidence for Ixekizumab in the Treatment of Psoriasis, Psoriatic Arthritis, and Axial Spondyloarthritis: Systematic Literature Review 2022–2023
Bibliographic record
Abstract
OBJECTIVE: To describe the results of a systematic literature review of real-world outcomes with ixekizumab in psoriasis (PsO), psoriatic arthritis (PsA), or axial spondyloarthritis (axSpA). METHODS: Databases, conference proceedings, and additional sources were searched for real-world studies in ≥ 25 patients treated with ixekizumab for PsO, PsA, or axSpA. Data on clinical effectiveness, patient-reported outcomes, treatment patterns, safety, and economic burden were extracted. RESULTS: A total of 118 publications were included, 96 in PsO, 16 in PsA, 5 in both PsO and PsA, and 1 in axSpA. Most focused on clinical effectiveness and treatment patterns. Ixekizumab was effective in real-world settings, and the anti-interleukin (IL)-17A biologics were more effective for skin clearance than comparator biologics. Anti-IL-17A biologics were effective for challenging body areas (nails, scalp, genitals, palmoplantar regions), and ixekizumab was associated with a higher chance of obtaining Dermatology Life Quality Index scores of 0/1 than secukinumab or other biologics. Ixekizumab was associated with generally high persistence/drug survival. No unexpected safety signals were identified. CONCLUSION: Real-world ixekizumab use for PsO and PsA is effective and safe, with a positive impact on patient quality of life. More data are needed to draw conclusions for real-world ixekizumab use in axSpA.
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".