Treatment modifying factors of biologics for psoriatic arthritis: a systematic review and Bayesian meta-regression.
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to explore factors that modify treatment effects of non-conventional biologics versus placebo in patients with psoriatic arthritis. METHODS: A systematic literature review and meta-regression was conducted. The biologics included etanercept, infliximab, adalimumab, golimumab, certolizumab, ustekinumab, tocilizumab, anakinra, abatacept, rituximab, and secukinumab. Outcomes included American College of Rheumatology (ACR) 20 and 50, Psoriasis Area Severity Index (PASI) 75, and 36-Item Short Form Health Survey (SF-36) Physical and Mental Component Summaries (PCS and MCS). RESULTS: Twelve RCTs were eligible for meta-regression. Treatment effects for ACR-20 at 12 weeks were higher in trials with longer disease durations (OR=2.94), and lower in trials enrolling older patients (OR=0.48), and those recently published (OR=0.49). Treatment effects for ACR-50 at 12 weeks were higher in trials with more males (OR=2.27), but lower in trials with high prior anti-TNF use (OR=0.28) and recently published trials (OR=0.37). For PASI-75, trials with more male patients (24 weeks: OR=2.56), and with higher swollen and tender joint counts (12 weeks: OR=8.33; 24 weeks: OR=14.44) showed higher treatment effects, and trials with high prior anti-TNF use had lower effects (OR=0.41). Treatment effects for SF-36 PCS at 24 weeks were higher in trials with longer psoriasis disease durations (OR=2.95) and PsA disease durations (OR=4.76), and those published earlier (OR=4.19). CONCLUSIONS: Our analyses show that differences in baseline characteristics may explain some of the differences in response to biologics versus placebo across different trials. Accounting for these factors in future studies will likely be important.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".