Ixekizumab (Interleukin 17A Antagonist): 12-week Efficacy and Safety Outcomes in Real-world Clinical Practice
Bibliographic record
Abstract
BACKGROUND:: Current knowledge of the efficacy and safety of ixekizumab is limited to data from phase III randomized controlled trials (RCTs). A gap exists in our understanding of treatment outcomes of this newly available biologic in real-world clinical practice. OBJECTIVE:: This study explores the efficacy and safety of ixekizumab in non-RCT patients to compare real-world outcomes to those reported in RCTs. METHODS:: We conducted a multicentre, retrospective chart review of patients treated with ixekizumab therapy for moderate-to-severe plaque psoriasis. Efficacy (Psoriasis Area and Severity Index score of 75 or Physician Global Assessment of 0 or 1) and safety (reported adverse events [AEs]) were assessed following a 12-week treatment period. RESULTS:: Of the 60 patients included, 45 (75.0%) achieved efficacious outcomes after 12 weeks of ixekizumab treatment. Twenty-two (36.7%) patients experienced one or more AEs, of whom only 3 (5.0%) withdrew from treatment as a result. Common AEs included injection site reaction/erythema/pain (13.3%) and dermatitis (5.0%). CONCLUSION:: Ixekizumab has shown to be a safe and effective therapeutic option for plaque psoriasis in real-world practice. It does not appear that patients experience more AEs in real-world clinics than those in clinical trials.
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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 | 0.032 | 0.055 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".