Efficacy of tildrakizumab for moderate‐to‐severe plaque psoriasis: pooled analysis of three randomized controlled trials at weeks 12 and 28
Bibliographic record
Abstract
BACKGROUND: Efficacy of tildrakizumab for plaque psoriasis was demonstrated in randomized, placebo-controlled trials. OBJECTIVE: To consolidate tildrakizumab efficacy results by pooling data. METHODS: Data (N = 2081) from tildrakizumab 100 mg, tildrakizumab 200 mg and placebo groups in three trials were pooled. RESULTS: Proportions of Psoriasis Area and Severity Index (PASI) 75 responders at week 12 were better with tildrakizumab 100 mg (62.3%) and tildrakizumab 200 mg (64.8%) vs. placebo (5.6%; P < 0.0001) and for PASI 90, PASI 100 and Physician's Global Assessment (PGA) 'clear' or 'minimal' vs. placebo (P < 0.0001). Responses increased from weeks 12 to 28. Week 12 PASI and PGA responses to tildrakizumab vs. placebo were numerically greater in patients with lower vs. higher bodyweight and were better with tildrakizumab 200 mg than tildrakizumab 100 mg for patients with higher bodyweight. Week 12 PASI 75 responses vs. placebo with tildrakizumab 100 mg were similar between patients with (55.0%) or without (56.7%) prior biologics. PASI 90, PASI 100 and PGA responses were generally higher in patients without prior biologics. Week 8 PASI 50 response predicted PASI 90 response. CONCLUSION: Pooled data confirmed the efficacy of tildrakizumab for moderate-to-severe plaque 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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".