Benefit–risk profile of tofacitinib in patients with moderate‐to‐severe chronic plaque psoriasis: pooled analysis across six clinical trials
Bibliographic record
Abstract
BACKGROUND: Although existing psoriasis treatments are effective and well tolerated in many patients, there is still a need for new effective targeted treatment options. Tofacitinib is an oral Janus kinase inhibitor that has been investigated in patients with moderate-to-severe chronic plaque psoriasis. OBJECTIVES: To consider the benefits and risks of tofacitinib in patients with moderate-to-severe psoriasis. METHODS: Data were pooled from one phase II, four phase III and one long-term extension study comprising 5204 patient-years of tofacitinib treatment. Efficacy end points included patients achieving Physician's Global Assessments of 'clear' or 'almost clear', ≥ 75% and ≥ 90% reduction in Psoriasis Area and Severity Index (coprimary end points) and improvements in Dermatology Life Quality Index score, Hospital Anxiety and Depression Scale depression score and Itch Severity Item score, at weeks 16 and 52. Safety data were summarized for 3 years of tofacitinib exposure. RESULTS: Tofacitinib 5 and 10 mg twice daily (BID) showed superiority over placebo for all efficacy end points at week 16, with response maintained for 52 weeks of continued treatment. Tofacitinib improved patients' quality of life and was well tolerated. Rates of safety events of interest (except herpes zoster) were similar to those in the published literature and healthcare databases for other systemic psoriasis therapies. Tofacitinib 10 mg BID demonstrated greater efficacy than 5 mg BID. CONCLUSIONS: Tofacitinib has a benefit-risk profile in moderate-to-severe psoriasis consistent with that of other systemic treatments.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".