Tumor Necrosis Factor Inhibitor Monotherapy Versus Combination Therapy for the Treatment of Psoriatic Arthritis: Combined Analysis of European Biologics Databases
Bibliographic record
Abstract
Objective. To investigate whether tumor necrosis factor inhibitor (TNFi) combination therapy with conventional synthetic disease-modifying antirheumatic drugs (csDMARD) is more effective for psoriatic arthritis (PsA) and/or improves TNFi drug survival compared to TNFi monotherapy. Methods. Five PsA biologics cohorts were investigated between 2000 and 2015: the ATTRA registry (Czech Republic); the Swiss Clinical Quality Management PsA registry; the Hellenic Registry of Biologics Therapies (Greece); the University of Bari PsA biologics database (Italy); and the Bath PsA cohort (UK). Drug persistence was analyzed using Kaplan-Meier and equality of survival using log-rank tests. Comparative effectiveness was investigated using logistic regression with propensity scores. Separate analyses were performed on (1) the combined Italian/Swiss cohorts for change in rate of Disease Activity Score in 28 joints (DAS28); and (2) the combined Italian, Swiss, and Bath cohorts for change in rate of Health Assessment Questionnaire (HAQ). Results. In total, 2294 patients were eligible for the drug survival analysis. In the Swiss (P= 0.002), Greek (P= 0.021), and Bath (P= 0.014) databases, patients starting TNFi in combination with methotrexate had longer drug survival compared to monotherapy, while in Italy the monotherapy group persisted longer (P= 0.030). In eligible patients from the combined Italian/Swiss dataset (n = 1056), there was no significant difference between treatment arms in rate of change of DAS28. Similarly, when also including the Bath cohort (n = 1205), there was no significant difference in rate of change of HAQ. Conclusion. Combination therapy of a TNFi with a csDMARD does not appear to affect improvement of disease activity or HAQ versus TNFi monotherapy, but it may improve TNFi drug survival.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".