Identifying and Quantifying the Role of Inflammation in Pain Reduction for Patients With Psoriatic Arthritis Treated With Tofacitinib: A Mediation Analysis
Bibliographic record
Abstract
INTRODUCTION: Pain is a multidimensional factor and core domain of psoriatic arthritis (PsA). This analysis aimed to quantify the role of potential inflammation-associated outcomes on pain reduction in patients with PsA receiving tofacitinib, using mediation modeling. METHODS: Pooled data were from two phase 3 studies (OPAL Broaden and OPAL Beyond) of patients with active PsA treated with tofacitinib 5 mg twice daily or placebo. Mediation modeling was utilized to quantify the indirect effects (via Itch Severity Item [ISI], C-reactive protein [CRP] levels, swollen joint count [SJC], Psoriasis Area and Severity Index [PASI], and enthesitis [using Leeds Enthesitis Index]) and direct effects (representing all other factors) of tofacitinib treatment on pain improvement. RESULTS: The initial model showed that tofacitinib treatment affects pain, primarily indirectly, via ISI, CRP, SJC, PASI, and enthesitis (overall 84.0%; P = 0.0009), with 16.0% (P = 0.5274) attributable to the direct effect. The model was respecified to exclude SJC and PASI. Analysis of the final model revealed that 29.5% (P = 0.0579) of tofacitinib treatment effect on pain was attributable to the direct effect, and 70.5% (P < 0.0001) was attributable to the indirect effect. ISI, CRP, and enthesitis mediated 37.4% (P = 0.0002), 15.3% (P = 0.0107), and 17.8% (P = 0.0157) of the tofacitinib treatment effect on pain, respectively. CONCLUSIONS: The majority of the effect of tofacitinib on pain was collectively mediated by itch, CRP, and enthesitis, with itch being the primary mediator of treatment effect. TRIAL REGISTRATION: NCT01877668, NCT01882439. GRAPHICAL PLS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".