Efficacy and safety of JAK inhibitors in the treatment of psoriasis and psoriatic arthritis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: JAK inhibitors are a relatively new class of medications that may be useful in the treatment of moderate-to-severe psoriasis and psoriatic arthritis (PsA). The objective of this study was to determine the efficacy of several JAK inhibitors in treating psoriasis and PsA and examine safety concerns. METHODS: MEDLINE, Cochrane and EMBASE were searched for randomized controlled trials and observational studies comparing any JAK inhibitor to placebo. The primary outcomes were a 75% improvement in the Psoriasis Area and Severity Index (PASI75) and a 20% improvement in the American College of Rheumatology composite score (ACR20). A secondary outcome was the proportion of patients achieving a "0" or "1" on the static Physician Global Assessment scale. Odds ratios were used to compare the proportion of patients reaching these targets in the max dose intervention group vs. the placebo group. A random effects model was used to account for heterogeneity. RESULTS: In total, 15 RCTs were included in the study and no observational studies. This encompassed 6757 patients in total. When the results were combined, the calculated odds ratio for PASI75 amongst tofacitinib vs. placebo was OR 14.35 [95%CI 7.65, 26.90], for PASI75 amongst non-tofacitinib JAK inhibitors vs. placebo it was OR 6.42 [95%CI 4.89, 8.43], for ACR20 amongst all JAK inhibitors versus placebo was OR 5.87 [95%CI 4.39, 7.85]. There was no significant difference in prevalence of serious adverse events between intervention and control in any of these studies. CONCLUSION: JAK inhibitors show promise for safely treating moderate-to-severe psoriasis and psoriatic arthritis.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".