Short- and long-term safety outcomes with ixekizumab from 7 clinical trials in psoriasis: Etanercept comparisons and integrated data
Bibliographic record
Abstract
BACKGROUND: Safety of biologics is important when treating patients with psoriasis. OBJECTIVE: We sought to determine the safety of ixekizumab in psoriasis. METHODS: Integrated safety data are presented from a 12-week induction period, a 12- to 60-week maintenance period, and from all ixekizumab-treated patients from 7 clinical trials. Exposure-adjusted incidence rates (IRs) per 100 patient-years are reported. RESULTS: Overall, 4209 patients received ixekizumab (total exposure: 6480 patient-years). During the induction period, the IRs of patients experiencing 1 or more treatment-emergent adverse event (AE) were 251 and 236 among ixekizumab- and etanercept-treated patients, respectively, and for serious AEs was 8.3 in both groups. During maintenance, for ixekizumab, the IRs of treatment-emergent AEs and serious AEs were 100.4 and 7.8, respectively. Among all ixekizumab-treated patients from 7 trials, the IR of Candida infections was 2.5. The IRs of treatment-emergent AEs of special interest (including serious infections, malignancies, major adverse cardiovascular events) were comparable for ixekizumab and etanercept during the induction period. LIMITATIONS: Additional long-term data are required. CONCLUSION: Ixekizumab had an acceptable safety profile with no unexpected safety findings during ixekizumab maintenance in psoriasis.
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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 | 0.024 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".