Comparison of cumulative clinical benefits of biologics for the treatment of psoriasis over 16 weeks: Results from a network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cumulative clinical improvement and speed of improvement are important to psoriasis patients. OBJECTIVE: Compare cumulative benefits of biologics over 12 to 16 weeks in the treatment of moderate to severe psoriasis. METHODS: A systematic literature review identified phase III trial data on Psoriasis Area and Severity Index (PASI) responses for biologics during 12 and 16 weeks of treatment. Cumulative clinical benefit, measured by the area under the curve for PASI ≥75% improvement (PASI 75), ≥90% improvement (PASI 90), and 100% improvement (PASI 100), was compared using the network meta-analysis and Bayesian methodology on the relative probability of achieving percentage of maximum area under the curve. RESULTS: Among biologics approved for psoriasis treatment, anti-interleukin-17 biologics demonstrated consistently greater cumulative clinical benefits on PASI 75, PASI 90, and PASI 100 over the 12- or 16-week period than anti-interleukin-23 and other biologics. For biologics with 12-week data, ixekizumab and brodalumab showed greater cumulative benefits for PASI 75, PASI 90, and PASI 100 than secukinumab, followed by guselkumab, infliximab, adalimumab, ustekinumab, and etanercept. Ixekizumab showed greater cumulative benefits than all other biologics reporting 16-week data. LIMITATIONS: Recently approved biologics were not included. CONCLUSION: Ixekizumab (at 12 weeks and 16 weeks) and brodalumab (at 12 weeks) had greater cumulative clinical benefit than all of other biologics studied.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".