Systematic literature review and network meta-analysis of therapies for psoriatic arthritis on patient-reported outcomes
Bibliographic record
Abstract
OBJECTIVES: Head-to-head clinical trials are common in psoriasis, but scarce in psoriatic arthritis (PsA), making treatment comparisons between therapeutic classes difficult. This study describes the relative effectiveness of targeted synthetic (ts) and biologic (b) disease-modifying antirheumatic drugs (DMARDs) on patient-reported outcomes (PROs) through network meta-analysis (NMA). DESIGN: A systematic literature review (SLR) was conducted in January 2020. Bayesian NMAs were conducted to compare treatments on Health Assessment Questionnaire Disability Index (HAQ-DI) and 36-item Short Form (SF-36) Health Survey including Mental Component Summary (MCS) and Physical Component Summary (PCS) scores. DATA SOURCES: Ovid MEDLINE (including Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily),Embase and Cochrane Central Register of Controlled Trials. ELIGIBILITY CRITERIA: Phase III randomised controlled trials (RCTs) evaluating patients with PsA receiving tsDMARDS, bDMARDs or placebo were included in the SLR; there was no restriction on outcomes. DATA EXTRACTION AND SYNTHESIS: Two independent researchers reviewed all citations. Data for studies meeting all inclusion criteria were extracted into a standardised Excel-based form by one reviewer and validated by a second reviewer. A third reviewer was consulted to resolve any discrepancies, as necessary. Risk of bias was assessed using the The National Institute for Health and Care Excellence clinical effectiveness quality assessment checklist. RESULTS: In total, 26 RCTs were included. For HAQ-DI, SF-36 PCS and SF-36 MCS scores, intravenous tumour necrosis factor (TNF) alpha inhibitors generally ranked higher than most other classes of therapies available to treat patients with PsA. For almost all outcomes, several interleukin (IL)-23, IL-17A, subcutaneous TNF and IL-12/23 agents offered comparable improvement, while cytotoxic T-lymphocyte-associated antigen 4, phosphodiesterase-4 and Janus kinase inhibitors often had the lowest efficacy. CONCLUSIONS: While intravenous TNFs may provide some improvements in PROs relative to several other tsDMARDs and bDMARDs for the treatment of patients with PsA, differences between classes of therapies across outcomes were small.
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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 | 0.056 | 0.148 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".