Dynamic Visual Representation of Clinical Efficacy of Ixekizumab in Psoriasis
Bibliographic record
Abstract
INTRODUCTION: Ixekizumab, a high-affinity monoclonal antibody that selectively targets interleukin-17A, is an approved treatment for plaque psoriasis. This study aimed to use animated visualizations as a tool to simplify complex data from ixekizumab clinical trials. METHODS: Animated visualizations were developed to show outcomes from ixekizumab clinical trials and a Bayesian network meta-analysis of 11 approved biologics. The visualizations simultaneously highlighted both aggregate scores and the individual progression of patients over the course of treatment. RESULTS: The animations provided key messages and information from the complex data in efficient and scientific ways that were also visually pleasing and simple to understand. The animations highlighted (1) rapid reduction in disease severity from baseline; (2) sustained efficacy of ixekizumab in the treatment of skin and nail psoriasis; (3) side-by-side comparisons of treatment efficacy and clinical improvement across trials; (4) simultaneous visual presentation of individual results with summary response over time; and (5) indirect comparison of relative treatment effects with other biologics based on Bayesian network meta-analysis. CONCLUSION: The rapid and sustained efficacy of ixekizumab in the treatment of psoriasis was demonstrated using multiple dynamic visualizations with different clinical endpoints. Animated visualizations provided a simpler and more comprehensive understanding of complex data than conventional static figures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".