Longterm Clinical Outcomes in 420 Patients with Psoriatic Arthritis Taking Anti-tumor Necrosis Factor Drugs in Real-world Settings
Bibliographic record
Abstract
OBJECTIVE: An observational study to evaluate the longterm clinical outcomes of adalimumab (ADA), etanercept (ETN), and infliximab (IFX) in patients with psoriatic arthritis (PsA), in real-world settings. METHODS: From a prospective cohort we studied 420 biologic-naive patients with PsA who had peripheral arthritis and were beginning a treatment with ADA, ETN, or IFX. Drug survival was evaluated by Kaplan-Meier life analysis, and baseline predictors of drug discontinuation were assessed by Cox regression analysis. The frequency of concomitant glucocorticoids and the daily mean dosage were compared by chi-square test and ANOVA repeated measures across 4 years. RESULTS: After 4 years the overall survival of the first anti-tumor necrosis factor-α (anti-TNF) was 51.0%, but significantly higher for ETN (58.9%) than ADA (43.9%) or IFX (44.0%; p = 0.003). Patients taking ETN also had the lowest HR of drug discontinuation (HR 0.57, 95% CI 0.34-0.93, p = 0.02). The strongest predictor of drug interruption was female sex (HR 2.02, 95% CI 1.28-3.20, p = 0.002). The disease duration was inversely correlated with drug discontinuation (HR 0.96, 95% CI 0.93-0.99, p = 0.02). The average daily dose of prednisone significantly decreased from baseline: 5.6 ± 2.5 to 4.7 ± 1.9 at 1 year (p = 0.01) to 4.0 ± 1.8 at 4 years (p = 0.001). Additionally, compared to baseline (49.6%), a significant reduction of patients taking glucocorticoids was detected at 2 years (36.5%, p < 0.05), 3 years (29.9%, p < 0.01), and 4 years (22.6%, p < 0.01). CONCLUSION: In real-world settings, TNF inhibitors showed a high rate of drug survival at 4 years. Further, the need for glucocorticoids for controlling active PsA was lowered with time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".