Treatment of Dactylitis and Enthesitis in Psoriatic Arthritis with Biologic Agents: A Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Biologic agents with different mechanisms of action [inhibitors of tumor necrosis factor-α (TNF-α), interleukin (IL)-12/23, and IL-17] showed efficacy in randomized controlled trials (RCT) in the treatment of psoriatic arthritis. We conducted a pooled metaanalysis of these agents for treatment of dactylitis and enthesitis and compared results with the American College of Rheumatology 20 (ACR20) response and Health Assessment Questionnaire-Disability Index (HAQ-DI) scores. METHODS: A systematic literature search was performed and a pooled metaanalysis of RCT with anti-TNF-α (infliximab, golimumab, adalimumab), anti-IL-12/23 (ustekinumab), and anti-IL-17 (secu kinumab, ixekizumab) was conducted using the random-effects model. Bias was assessed using the Cochrane risk-of-bias tool. RESULTS: Eighteen RCT were included in the pooled analysis (n = 6981). Both TNF-α inhibitors and novel biologics (ustekinumab, secukinumab, ixekizumab) demonstrated significant resolution of dactylitis at Week 24 with pooled risk ratios (RR) versus placebo of 2.57 (95% CI 1.36-4.84) and 1.88 (95% CI 1.33-2.65), respectively. For resolution of enthesitis at Week 24, RR for TNF-α inhibitors was 1.93 (95% CI 1.33-2.79) versus 1.95 (95% CI 1.60-2.38) for novel biologics. Both biologic categories showed overlapping ranges of ACR20 responses (TNF-α inhibitors: RR = 2.23, 95% CI 1.60-3.11; pooled IL-12/23 and -17: RR = 2.30, 95% CI 1.94-2.72) and similar quality of life improvement scores with mean HAQ-DI score changes of -0.29 (95% CI -0.39 to -0.19) and -0.26 (95% CI -0.31 to -0.22), respectively. CONCLUSION: The pooled analysis demonstrated that anti-TNF-α agents have the same efficacy as novel agents (ustekinumab, secukinumab, and ixekizumab) in dactylitis and enthesitis.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".